Extending the use of scenario planning and MCDA for the evaluation of strategic options

Figures & data

Table 1 Summary of MCDA and scenario planning interventions to date

Reference	Number of scenarios	Approach to scenario construction	Method of elicitation		Recommendations for applying MCDA measure ie Σj(vkir·wir)
			Weights (wir)	Values (vkir)
Goodwin and Wright (2001)	2 ‘extreme world’ scenarios; no probabilities attached	Extreme world narratives created by putting all negatively and positively resolved uncertainties in separate scenarios and then checking for internal consistency	• Compare 0–100 for each criterion ci	• Allocate a score of 100 to the best strategy–scenario combination and 0 to the worst in terms of its performance under each criterion	• Use scenario matrix to establish dominance of strategies across scenarios
			• Assign 100 to the most important criterion and compare it with the relative importance of other swings on a 0–100 scale	• Allocate scores between 0 and 100 to represent the improvement in criterion ci that the application of strategy ak may be perceived to bring in scenario yr for all yrɛY	• Perform sensitivity analysis
			• Normalise scores obtained		• Evaluate risk by including a risk criterion in the MCDA model
Belton and Stewart (2002)	2 scenarios	Scenario narratives are external to the organisation (ie, arise from events outside the organisation's control) and are value-free	Different weights assigned for each scenario using approach as in Goodwin and Wright (2001)	Best and worst performances against each criterion across all scenarios define upper and lower limits of the value scale	• Select option that is robust against uncertainties (a direct holistic judgment)
				Allocate scores between 0 and 100 to represent the improvement in criterion ci that the application of strategy ak may be perceived to bring in scenario yr	• Assign relative weights to scenarios and calculate a one-dimensional performance value for each alternative by multiplying the weight of each scenario by MCDA measure
					• Select option that maximises the worst aggregate performance given by the sum of all MCDA measures over all scenarios. This however ignores trade-offs between performances
Montibeller et al (2006)	2–3 scenarios	Narratives based on best; worst and extrapolation of present trends/themed according to one key uncertainty; no probabilities attached	As in Goodwin and Wright (2001) but may assign a different set of weights for each scenario	Focus on a single scenario when expressing preferences as in Belton and Stewart (2002)	• Identify options that are nearer to the ideal performance (100) and that have stable performances close to the ideal
					• Evaluate risk by calculating inter-scenario risk, defined by assessing spread of performances for each option in each scenario
					• Remove dominated strategies if uncertainty is low; otherwise perform sensitivity analysis
Stewart (2005)	Suggests 3–5 scenarios may be sufficient	Unspecified	Treat all criteria–scenario combinations as ‘meta-criteria’	Assumes intensity of preference for different increments in performance on any one criterion may differ from scenario to scenario. Guidance not explicitly provided on elicitation	• Outranking to generate classification into preferences classes
					• Goal programming to measure achievement in terms of distance from a goal or reference level
Phillips (1986)	3 scenarios	High, medium and low based on demand uncertainty, which corresponds to optimistic and pessimistic considerations; probabilities attached	Relative weights elicited which reflect the importance of moving from 0 to 100 on each criterion scale. Weights can also represent the probability of each scenario	As in Belton and Stewart (2002)	• Find differences among best strategies in each scenario and find ways of managing them through further analysis or information

Figure 1 The six steps in the proposed method.

Table 2 Key uncertainties and the spectrum of possible discrete values

	Severity of natural disasters	Regulation in supplier countries	State of global economy	Consumer demand for safe foods	Cost of farming inputs
Most preferable (best)	Low	Supportive	Positive growth	High	Low
Least preferable (worst)	High	Neutral	Stagnation	Low	Moderate
		Restrictive	Negative growth		High

Table 3 Elicited value for how strategies are perceived to perform with respect to each criterion in various scenarios

vkir	Scenario
	BBBBB	WBBBB	BWBBB	BBWBB	BBBWB	BBBBW	WWWWW	BWWWW	WBWWW	WWBWW	WWWBW	WWWWB
Quantity (C1)
A	0	100	70	25	100	100	40	100	90	0	100	0
B	100	75	100	100	75	90	60	90	80	50	75	80
C	60	50	60	40	0	50	80	80	0	70	50	60
D	80	0	70	50	85	60	100	40	75	100	0	70
E	95	70	0	0	80	0	0	0	100	80	90	100
Quality (C2)
A	0	100	100	60	100	100	80	90	100	75	80	0
B	85	95	70	100	80	90	60	75	60	70	100	70
C	100	90	60	60	70	70	100	100	25	75	0	100
D	60	0	75	70	80	0	95	0	30	0	75	65
E	90	80	0	0	0	80	0	60	90	100	65	75
Cost (C3)
A	75	100	60	30	100	25	15	0	25	100	10	25
B	90	60	0	100	0	0	30	65	40	65	30	40
C	100	10	100	70	95	100	90	90	85	90	95	85
D	95	50	80	75	90	70	100	100	100	85	100	100
E	0	0	50	0	60	40	0	40	0	0	0	0

Figure 2 Sample scenario narrative for BBBBB.

Table 4 Estimation of what elicited values translate to on respective criteria scales

	BBBBB			WWWWW
	Quantity* (in US$)	Quality	Cost (in US$)	Quantity (in US$)	Quality	Cost (in US$)
A	−550 million	45% of RDI	330 million	−400 million	35% of RDI	250 million
B	200 million	65% of RDI^†	25 million	−480 million	27% of RDI	20 million
C	−300 million	85% of RDI	1 million	−550 million	50% of RDI	1.5 million
D	−200 million	57% of RDI	3 million	−300 million	40% of RDI	2 million
E	150 million	75% of RDI	1 billion	−700 million	25% of RDI	950 million

*Balance of payments for food=total imports−total exports. The negative (positive) value represents the amount of reduction (increase) in the balance of payments from its current value.

^†RDI—recommended daily intake of basic nutrients per capita.

Table 5 Normalised criterion weights for scenarios

Wik	BBBBB	WBBBB	BWBBB	BBWBB	BBBWB	BBBBW	WWWWW	BWWWW	WBWWW	WWBWW	WWWBW	WWWWB
Quality	0.255	0.097	0.385	0.156	0.217	0.269	0.204	0.296	0.264	0.143	0.275	0.235
Quantity	0.426	0.645	0.288	0.4	0.435	0.346	0.388	0.334	0.358	0.476	0.333	0.373
Cost	0.319	0.258	0.327	0.444	0.348	0.385	0.408	0.370	0.377	0.381	0.392	0.392

Table 6 Overall performance of how strategies perform under various scenarios

Performance Σj(vkir·wir)	BBBBB	WBBBB	BWBBB	BBWBB	BBBWB	BBBBW	WWWWW	BWWWW	WBWWW	WWBWW	WWWBW	WWWWB
A	23.9	100	78.3	32.7	100	71.1	38	60	68	48.8	59.2	9.8
B	93	73.1	55.8	100	50	55.4	47.8	76.3	59.6	58.6	64.2	62
C	83	43.6	73.1	56.4	48.3	74.6	88.2	89.6	38.7	78.3	53.9	79.2
D	79.7	12.9	75.2	64.2	85.7	47.7	99	50.4	72.5	80	59.8	80.6
E	63.4	52.9	16.4	0	55.7	36.9	0	32.6	59.6	52.4	47.9	54.9

Table 7 Worth-equivalent regret values for each strategy–scenario combination

Cost equivalent regret (US$million)	BBBBB	WBBBB	BWBBB	BBWBB	BBBWB	BBBBW	WWWWW	BWWWW	WBWWW	WWBWW	WWWBW	WWWWB
A	2054.5	0	0	1438.9	0	86.4	1419.3	758.7	111.4	776.1	121.4	1713.6
B	0	990.6	653.9	0	1363.9	475.2	1191.4	341.4	325.0	533.3	0	450.7
C	298.2	2076	150.9	931	1411.2	0	251.7	0	851.5	41.1	250.4	33.5
D	395.7	3203.8	89.5	764.8	391.2	663.6	0	1007	0	0	106.8	0.0
E	879.5	1732.1	1797.3	2137.4	1208.6	929.5	2302.3	1463.5	325.0	687.6	396.8	621.2

Figure 3 Plot of cost equivalent regret values.

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