SEGUNDO CICLO DE SELECCIÓN Y RECOMBINACIÓN (C2)

One question considered whether there was a need for a decision support tool to assist in the implementation of WPD. Ninety-five percent (95%) of the respondents confirmed that they would find a decision-making framework useful.

The remaining results in regard to the requirements for a decision-making framework are subdivided into sections relating to each phase of the decision-making process (see section 2.2.2.1). The first section investigates the industrial requirement for problem structuring. The second section investigates the requirement for a structured decision analysis. The final section addresses the responses related to the need for a

21.1% 31.6% 26.3% 42.1% 73.7% 0% 20% 40% 60% 80% 100% Detailed design Flowsheet design Process development Process concept Route selection

post analysis study and issues pertaining to the design of a decision-making framework.

3.3.3.1 Problem Structuring

As discussed in section 2.2.2.1, the basis of problem structuring is to identify suitable criteria and alternatives for a decision problem. Only a quarter (26%) of the respondents said that they find it difficult to provide appropriate names for their criteria (i.e. to describe a measure that can be perceived by everyone in the company). However, over half (53%) of the respondents find it difficult to select a source to measure their criteria (for example, using the LD50 index to measure “Chemical Toxicity”). This could be due to the nature of the data available as all of the respondents said that their decisions were influenced by both qualitative and quantitative information.

With regards to identifying suitable alternatives (for example, chemical routes for a route selection problem), the respondents were asked if their decision problems comprised of a fixed number of alternatives or an infinite number of solutions. All respondents stated that they selected from a fixed number of alternatives with the majority (93%) selecting a small number of viable options from a larger collection of conceivable solutions. The remaining 7% of the respondents said they always make decisions from a small finite number of alternatives.

In terms of identifying criteria and alternatives, the respondents were asked if they preferred brainstorming by the use of a mind map or a list (e.g. pros and cons). Eighty six percent (86%) of the respondents favoured brainstorming via a list.

3.3.3.2 Decision Analysis

The need for a guidance tool as proposed by Robinon Brothers was investigated by asking whether the respondents would have favoured a system that produces exact results with a lengthy data entry procedure, or a system that guides the user in the right direction quickly. Eighty nine percent (89%) opted for the latter. This result clearly indicates the preference for a Multi-Attribute (MA) or outranking based approach as opposed to a Multi-Objective Optimisation (MOO) procedure.

The issue of the maximum time the respondents typically have available to analyse an important decision problem was considered. From Figure 3-2, 69% of the respondents would spend one hour or less analysing a decision problem. This renders a number of Multi-Criteria Decision Analysis (MCDA) techniques infeasible. For example, Doumpos and Zopounidis (2004) found that MCDA methods that require threshold values, such as ELECTRE and PROMETHEE, to be exceptionally time consuming to the extent of inhibiting real-world application. Likewise, Lootsma (1999) found MCDA methods which utilise pairwise comparisons, such as the Analytic Hierarchy Process, “complicated and time-consuming”.

Figure 3-2 Maximum time the respondents have to solve a decision problem

Some MCDA methods require criteria to be represented by distributions, such as PROMETHEE. The respondents were asked if they would be comfortable selecting an appropriate distribution shape to define each of their criteria. As illustrated in Figure 3-3, the majority (67%) of the respondents indicated that they would only be able to select distributions under much guidance.

The respondents were also questioned regarding the inputs and outputs of a decision analysis. With regard to input, the respondents were asked which qualitative selection scale they would prefer from three options: small scale (1-5), medium scale (1-9) or large scale (1-100). Fifty three percent (53%) preferred small scale, 47% medium scale and 0% large scale.

With regard to output, the respondents were asked if they would prefer their results in the form of numerical values or a ranking. The results were inconclusive with 47% preferring numerical values and the remaining asking for a ranking.

five minutes, 16% thirty minutes, 32% one hour, 21% one day, 26% two to three days, 5%

Figure 3-3 Percentage of respondents who feel comfortable using utility functions

3.3.3.3 Post Analysis and Design Features

The remaining series of questions focused on identifying the importance of design features, including the requirements for a post analysis study (see section 2.2.2.1). The respondents were asked to weight the importance of seven features on a scale of 1-10 (1 being extremely unimportant and 10 being extremely important). The results of the study are summarised in Figure 3-4. Five values can be identified from each plot, the lowest score, lower quartile (bottom of the red box), median (where red and blue meet), upper quartile (top of blue box) and the highest score. An asterisk represents an outlying score.

C1 Intuitive user interface C5 Functionality for a sensitivity analysis

C2 Speed of operation and user input C6 Support for group decision-making

C3 Influence from past decision-making or knowledge C7 Functionality to record justification behind selections

C4 Compatibility with different operating systems

Figure 3-4 Box plot of the importance of certain design features

13% 7%

67% 13%

Yes.

Under some guidance. Under much guidance. No.

C1 Intuitive user interface scored highly as the ease of operation is essential to ensure that the framework can be used rapidly and appropriately.

C2 Speed of operation and user input scored highly. This correlates with the responses shown in Figure 3-2.

C3 The high score for influence from past decision knowledge correlates with the results of the two interviews (section 3.2.3).

C4 The requirement for compatibility with different operating systems received a varied response. Compared to the other design features, C4 was the least preferred design feature by the respondents.

C5 Besides one outlier, the majority of the respondents favoured the capability of a sensitivity study in the post analysis phase of the decision process.

C6 Besides two outliers, the most sought after design feature was the ability to perform group decision-making. To confirm this, the respondents were also asked if they make decisions face-to-face in a group and/or need to consider external stakeholders (e.g. shareholders). Eighty seven percent (87%) of the respondents make decisions in a group environment and 80% need to consider external stakeholders.

C7 Functionality to record justification for each selection in a decision analysis scored highly. This feature allows for decision data to be stored for future decision-making. Therefore, this high score correlates with the requirement identified in the interviews to retrieve and reuse past decision knowledge.

3.4 Conclusions

The aim of this chapter was to address the following three research questions:

RQ4: What techniques are currently being used for decision-making in industry?

RQ5: What are the most common decisions made in WPD and in what stage of development are they considered?

Therefore the conclusions are presented in three sections, addressing each question in sequence.