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Systems Engineering Procedia

Systems Engineering Procedia 00 (2011) 000–000

Systems Engineering Procedia 4 (2012) 196 – 202

www.elsevier.com/locate/procedia

The 2nd International Conference on Complexity Science & Information Engineering

Understanding the complex nature of engineering technology selection: A new methodology based on systems thinking Ju Xiaofenga, Jiang Penga, Yin Yunb a* a

Harbin Institute of Technology, 92 Xidazhi Street, Harbin 150001,China b

Systemic Consult, 15 Great Parks, Holt, Trowbridge, BA14 6QP, UK

Abstract This paper aims to apply systems thinking to address the complexity in engineering technology selection problems. Although previous researches have been conducted in this area, few made attempts to emphasize the importance of the connected nature of engineering technology selection problems and thus there is a need to bridge such a gap. In this paper, we symbolize conventional technology selection approaches as Basic Decision Equations (BDE) and introduce a connectivity matrix and processing methods so as to arrive at simple and comprehensive views of the nature of engineering technology selection problem. Discussions are made upon a case study using this proposed method.

©©2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of Desheng Dash Wu. 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of Desheng Dash Wu

Open access under CC BY-NC-ND license.

Keywords: systems thinking, complexity, engineering, technology selection

1. Introduction The increasing unpredictability in the business world exposes all participants to unprecedented uncertainty and selecting technologies is of strategic importance. Torkkeli and Tuominen (2002) [1] point out that committing limited resources to promising new technology is closely related to a company’s core competencies and fully exploring the cognitive structure of decision makers can enhance decision making performance. A systematic procedure is often needed before making such strategic decisions as selecting new technologies. Due to the inherent complexity of engineering technology selections, many researchers and practitioners and researchers, such as Chan (2000) [2], Shehabuddeen et al (2006) [3], Kuei et al (1994) [4], propose to adopt systems approaches to solve the problems. However, few of them provide practical solutions to operationalize systems thinking in the domain. Therefore, we attempt to utilize systems thinking and techniques to complement existing literatures.

* Corresponding author. Jiang Peng. Tel.: +86-151-1467-0658; fax: +86-0451-8640-0653. E-mail address: [email protected].

2211-3819 © 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of Desheng Dash Wu. Open access under CC BY-NC-ND license. doi:10.1016/j.sepro.2011.11.066

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2. Background review Quite a few studies have been conducted in the technology selection in relation to the engineering fields. Cabral-Cardoso and Payne (1996) [5] categorize practical technology selection methods into mathematical programming methods, multifactor techniques and expert systems and economic and financial techniques, whilst Henriksen and Traynor (1999) [6] conclude that practical methods include unstructured peer review, scoring, mathematical programming, economic models, decision analysis, interactive methods, artificial intelligence, and portfolio optimization. However, there are inherent limitations in these methodologies. Firstly, a new method may not be well synchronized with other existing decision making techniques. Secondly, many ‘systems’ or ‘packages’ make trade-off between simplicity and comprehensiveness. Thirdly, many of current methodologies are not user-friendly to participants who are not specialized in decision making. Therefore, it is necessary to develop a methodology, which can utilize existing techniques and tools, to provide concise and comprehensive understandings of technology selection problems from systems (strategic) level. On the other hand, systems thinking aims to recognize, organize, analyze, and resolve problems systemically. Some mathematic techniques, such as graph theories and factor analysis, can be employed to explore the cognitive mappings. In these regards, establishing a methodology to operationalize systems thinking can benefit the decision makers when they face technology selection problems. 3. The Proposed Method Systems thinking can help decision makers arrive at a holistic understanding of a complex problem (Checkland and Scholes, 1990) [7]. To operationalize the concept in technology selection problem, we highlight four aspects: considering technology selection environment in a broad range; emphasizing the importance of relationships between decision elements; taking a long term vision to the technology selection; delivering rich information whilst keeping methodology simple. Given a firm has already acquired technology selection capabilities, the core includes decision factors, decision criteria, decision techniques and decision models (Goodwin and Wright, 2004) [8], which can be expressed in the following equation

= T f ( g (α,β), σ )

(1)

where T is the output of the technology selection, f is the decision model in use, alpha is the decision factors, beta is the decision criteria, g is the techniques to process alpha against beta, and sigma is other factors affecting the decision making. We call Equation (1) the basic decision equation (BDE). For the first step of the proposed methodology, we assume that decision makers are able to draw the boundary of the problem and measure all relevant elements using BDE, including all short term and long term ones. Then, they are invited to describe the relationships between elements pairwisely. Often, a technology selection problem involves n alternative technologies or elements, each of which is able to be measured as a T value. Thus, the connectivity between two elements is

Cij = c(Ti , T j )

(2)

where T is the BDE score of an element, i and j represents the elements respectively, and c is the function to express the relationship between two elements. We call c as connectivity and thus Cij is the connectivity value between two elements of the system. Since connectivity values are derived pairwisely, all connectivity values can be allocated into the following matrix:

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1 C12 C13 ... C1z    C21 1 C23 ... C2z  Ci1........Cij ........    ...  C C ...........1   z1 z2 

(3)

where i and j represents the i-th and j-th element and z is the total number of elements within the system. Thus, the BDE values of different elements can be re-measured in a systems context:

T=' F ( Τ, c )

(4)

where c is the connectivity matrix, T is the BDE value, and F is the mathematic consideration of the connectivity values and the BDE value. So, T’s is the value of an element in the system. For the sake of simplifying the calculation of Equation (4), we follow the method introduced by Allan and Yin (2011) [9] to measure relative importance of BDE values. Firstly, a pair of elements’ relative importance value can be obtained as:

Tij = (Ti + T j ) × Cij

(5)

Then, all the pairwise values can be located into the strategic selection matrix (6).As each value on the diagonal of the matrix influences both the column and row in which it locates, it is rational to normalize the matrix and to elicit the relative importance. The method we used for this is similar to one of the normalization methods that are proposed by the Analytical Hierarchy Process (AHP) (Saaty, 1980) [10], which is: 1. calculate the mean value of a row and locate it into every grid in the row; 2. calculate the mean value of the column and locate it into every grid in the column; 3. calculate the weighted value of every gird by averaging column and row values for that grid. T11 T12 T13... T1Z  T T T ... T  2Z   21 2 23 Tm1........Tmn ......    ...  TZ1 TZ2 ,..........TZZ 

(6)

The results can be demonstrated in Matrix (7). For any grid in Matrix (7), the normalized, or the relative importance, is called as normalized strategic selection value and is calculated as Equation (8). T'11 T'12 T'13... T'1Z  T' T' T' ... T'  2Z   21 2 23 T' m1.........T' mn ......    ...  T' Z1 T' Z2 ..........TZZ  z

T 'mn =

∑T m =1

mn

(7)

z

+ ∑ Tmn 2z

n =1

(8)

In graph theory, every value in an adjacency matrix can indicate the causal or other logic relationships between the two variables, including the connectiveness (Wilson, 1996) [11]. Matrix (7) can

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be transformed into an adjacency matrix by selecting criteria to determine whether technology selection value is strategic or not. Then, elements of the system (technology selection alternatives and other issues throughout the perceivable lifecycle) can be displayed as nodes and Strategic Selection Values can be viewed as the edges between nodes. 4. The Numerical Case Study The case study is based on an engineering company’s technology selection problem. At current stage, the company has four technology options available, namely A, B, C and D. On the other hand, this technology selection involves other issues, including E, F, G, H, and I. Issues, such as plant location, competitive scope and etc, are relevant to the decision making problem and they are symbolized as J, K, L and M respectively. Details can be found in table 1. Table 1 Normalized BDE values.

Initial BDE Values Normalized BDE Values Technology Alternatives A. Technology A

0.50

0.3983

B. Technology B

0.40

0.3891

C. Technology C

0.40

0.3685

D. Technology D

0.64

0.5103

E. Supplier selection

0.08

0.2080

F. Staff training

0.25

0.2986

G. Manufacturing system upgrade

0.16

0.2326

H. Expand R&D team

0.16

0.2348

I. Product safety

0.05

0.1633

J. Market expansion

0.40

0.4211

K. Location selection

0.02

0.1132

L. Knowledge creation and retention

0.02

0.0530

M. New technology options

0.04

0.1537

Issues on System Level

Long Term Strategic Issues

Once initial BDE values are obtained, shown as in Table 1, all of the decision makers are invited to evaluate the connectivity values between any two factors to establish the pairwise relationship. After obtaining the connectivity values, participants come up with the matrix（9）:  A  B  C D  E F  G H  I J  K L   M

A 1.00 0.80 0.20 0.50 0.50 0.50 0.50

B 0.10 1.00 0.50 0.20 0.50 0.50 0.50

C 0.10 0.80 1.00 0.20 0.50 0.50 0.50

D 0.10 0.80 0.80 1.00 0.50 0.50 0.50

E 1.00 1.00 1.00 1.00 1.00 0.80 0.80

F 1.00 1.00 1.00 1.00 0.80 1.00 0.10

G 1.00 1.00 1.00 1.00 0.20 0.50 1.00

H 1.00 1.00 1.00 1.00 0.50 0.80 0.20

I 1.00 1.00 1.00 1.00 0.10 0.10 0.05

J 0.80 0.80 0.80 0.80 0.50 0.50 0.50

K 0.80 0.80 0.80 0.80 0.80 0.80 0.80

L 0.20 0.20 0.20 0.20 0.50 0.50 0.50

0.50 0.50 0.80 0.20 0.05 0.50

0.50 0.50 0.80 0.20 0.05 0.50

0.50 0.50 0.80 0.20 0.05 0.50

0.50 0.50 0.80 0.20 0.05 0.50

0.10 0.10 0.80 0.20 0.20 0.05

0.20 0.10 0.80 0.20 0.20 0.05

0.10 0.10 0.80 0.20 0.20 0.05

1.00 0.05 0.80 0.20 0.20 0.05

0.05 1.00 0.80 0.20 0.20 0.05

0.50 0.50 1.00 0.50 0.10 0.20

0.80 0.80 0.80 1.00 0.10 0.05

0.50 0.50 0.50 0.05 1.00 0.05

M  0.80  0.80   0.80  0.80  1.00  1.00   1.00  1.00  1.00  0.20   0.80 0.20   1.00 

(9)

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Ju Xiaofeng et al. / Systems Engineering Procedia 4 (2012) 196 – 202  A  B  C D  E F  G H  I  J K  L  M

A B C D E F G H I J K L M  0.3983 0.3492 0.3584 0.4373 0.3434 0.3814 0.3555 0.3638 0.3197 0.4072 0.3187 0.2579 0.3244  0.4382 0.3891 0.3983 0.4773 0.3833 0.4213 0.3954 0.4037 0.3596 0.4472 0.3586 0.2978 0.3643   0.4082 0.3591 0.3683 0.4473 0.3533 0.3913 0.3654 0.3737 0.3296 0.4172 0.3286 0.2678 0.3343  0.4712 0.4221 0.4313 0.5103 0.4163 0.4543 0.4284 0.4367 0.3926 0.4802 0.3916 0.3308 0.3973   0.2629 0.2138 0.2230 0.3019 0.2080 0.2459 0.2200 0.2284 0.1843 0.2718 0.1832 0.1225 0.1890  0.3155 0.2664 0.2756 0.3545 0.2606 0.2986 0.2727 0.2810 0.2369 0.3244 0.2358 0.1751 0.2416   0.2754 0.2263 0.2356 0.3145 0.2206 0.2585 0.2326 0.2409 0.1968 0.2844 0.1958 0.1350 0.2015 0.2693 0.2202 0.2294 0.3084 0.2144 0.2524 0.2265 0.2348 0.1907 0.2783 0.1897 0.1289 0.1954  0.2419 0.1928 0.2021 0.2810 0.1871 0.2250 0.1991 0.2074 0.1633 0.2509 0.1623 0.1015 0.1680   0.4122 0.3630 0.3723 0.4512 0.3573 0.3952 0.3693 0.3776 0.3336 0.4211 0.3325 0.2717 0.3382  0.2128 0.1637 0.1730 0.2519 0.1580 0.1959 0.1700 0.1783 0.1342 0.2218 0.1332 0.0724 0.1389  0.1934 0.1443 0.1535 0.2325 0.1385 0.1765 0.1506 0.1589 0.1148 0.2023 0.1138 0.0530 0.1195  0.2277 0.1785 0.1878 0.2667 0.1728 0.2107 0.1848 0.1931 0.1491 0.2366 0.1480 0.0872 0.1537 

(10)

The normalized strategic selection matrix illustrates two important properties: the relative importance of each variable and their time sequence. Furthermore, transforming the Normalized Strategic Matrix into simpler format may enhance decision makers’ sensing making. In this regard, decision makers shall select a BDE value to filter information. And then an adjacency matrix can be obtained as Matrix (11): A  A 0  B 1  C 1 D 1  E 1 F 1  G 1 H 1  I 1  J 1  K 1  L 0  M 1

B C 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0

D 1 1 1 0 1 1 1 1 1 1 1 1 1

E F 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 1

G H I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0

J 1 1 1 1 1 1 1 1 1 0 1 1 1

K 1 1 1 1 0 1 0 0 0 1 0 0 0

L M 1 1 1 1  1 1 1 1  0 0 0 1  0 1 0 0  0 0  1 1 0 0  0 0 0 0 

Then, the matrix can be visualized with the help of graph theories as following：

Fig. 1. Strategic Selection Value = 0.2

(11)

Ju Xiaofeng et al. / Systems Engineering Procedia 4 (2012) 196 – 202

Fig. 2. Strategic Selection Value = 0.4

In this diagram, the vertical axis represents the importance and the horizontal axis represents the time horizon, i.e. short term, medium term and long term. Nodes are the variables and edges connect different nodes. Thus, different variables are visualized. The increase in the strategic selection value cuts off the connections and hence reduces the complexity of the diagram. With the increase of the strategic selection value, a diagram’s complexity can reduce to a more manageable level. For example, if the strategic selection value is 0.4, a new graph would be as Figure 2. 5. Discussions In this case study, decision makers cannot differentiate B and C only from BDE values. After applying the connectivity measurement, we can obtain new insights as B is more connected to C. This is helpful in practice. Firstly, a systems approach can provide alternative solution to a problem holistically. By taking a systems approach to look at all possible connections, decision makers are allowed to understand the problem in a broader context, explaining why B is more important than C. Secondly, the differences between original BDE and Normalized BDE values are in accordance with a decision maker’s mental construct. For some variables the Normalised BDE values are smaller than original ones, whilst the Normalised BDE values may be greater than the originals in other occasions. In a decision maker’s concept model, if a variable is more connected to others, it can exert more influences on others as well as be influenced by others, leading to greater connectivity values, which is finally reflected as an increase in the Normalised BDE value. For a decision maker, a sharp increase in BDE value, i.e. E. supplier selection and L. product safety, may indicate that the decision maker should rethink about that variable’s importance in the whole system. Introducing a connectivity matrix to existing technology selection methodologies (symbolised as the BDEs) not only offers a platform to understand holistic contexts but also enables systems thinking style. Furthermore, the proposed methodology provides a visualized quantitative method for technology selection problems. When the decision makers select a strategic selection value (actually a normalized BDE value) with some criteria, Matrix (11) can be transformed into a graph which is represented by edges

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and nodes. Interpretation can be easily made. For instance, if the company chooses Technology D, it is very likely for the company to achieve the objectives of E, F G and a further goal of J. Additionally, the proposed methodology allows for a cooperative and reflective decision making attitude, which echoes the essence of Soft Systems Methodology (Checkland and Scholes, 1990). 6. Conclusions In this paper we briefly reviewed existing literatures about technology selection problems in the engineering field. Due to inherent complexity in business environment, most of the existing methodologies and techniques fail to balance contextual meaningfulness and methodological simplicity. We propose to apply systems thinking in a programmatic way in engineering technology selections. In doing so, we emphasize that scope and time of engineering technology selection problem is important and taking interconnected nature of the problem into consideration can help us arrive at a holistic view. In brief, the proposed methodology is based on existing decision techniques and integrates a connectivity issues into calculation. With graph theory, the outputs can be visualized and hence provide concise and direct demonstrations. In the case study section, we visualize the outputs in diagrams. By interpreting the map-like outcomes, several implications can be drawn, such as the technology development route. References [1] Torkkeli M. and Tuominen M. (2002) The contribution of technology selection to core competencies, International Journal of Production Economics, 77, 271-284. [2] Chan, Y. E. (2000) IT value: The great divide between qualitative and quantitative and individual and organizational measures. Journal of Management Information Systems, 16[4], 225–261. [3] Shehabuddeen N., Probert D., and Phaal R., (2006) From theory to practice: challenges in operationalizing a technology selection framework," Technovation, 26, 324–335. [4] Kuei, C. H., Lin, C., Aheto, J. and Madu, C. N. (1994) A strategic decision model for the selection of advanced technology. International Journal of Production Research, 32[9], 2117-2130. [5] Cabral-Cardoso, C. and Payne, R. L. (1996) Instrumental and Supportive use of Formal Selection Methods in R&D Project Selection. IEEE Transactions on Engineering Management, 43[3], 402-410. [6] Henriksen, A. and Traynor. A. (1999) A Practical R&D Project-Selection Scoring Tool. IEEE Transactions on Engineering Management, 46[2], 158–170. [7] Checkland, P. and Scholes, J. (1990) Soft systems methodology in action, Chichester, Wiley. [8] Goodwin, P. and Wright, G. (2004) Decision Analysis for Management Judgment, Wiley, London. [9] Allan, N. and Yin, Y. (2011) A methodology for understanding the potency of risk connectivity, Journal of Management in Engineering, 27 [2], 75-79. [10] Saaty,T.L.(1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw Hill, New York. [11] Wilson R.J. (1972) Introduction to graph theory, Longman, New York.