Characteristics of construction innovation include early problems; ..... Rational analysis for a problematic world, Wiley, Chichester. Ferrell, W. R., 1994. Discrete ...
COBRA 1999 Scenario planning for implementing construction innovation I A Motowa, A D F Price and W Sher, Loughborough University
ISBN 0-85406-968-2
SCENARIO PLANNING FOR IMPLEMENTING CONSTRUCTION INNOVATION I. A. Motawa, A.D.F. Price and W. Sher Dept. of Civil and Building Eng., Loughborough University, UK
ABSTRACT Innovation within the highly fragmented construction industry operating within an uncertain environment requires further development for the traditional planning techniques. Uncertainty that cannot be resolved cannot affect the consequence of the decision analysis to implement innovations. However, improved performance gained from adopting more innovative approaches requires commitment, uncertainties affected that innovations require flexibility to manipulate these uncertainties. This paper presents a modelling technique, currently under development, that simulates construction innovation implementation by their nature of experimentation, iteration and refinement activities. The proposed model targets balancing commitment and flexibility. This can be achieved by scenario planning designed to reflect the same prediction with different outcomes for uncertainties. Scenarios based on causality are suitable for rational reasoning processes, and mental models negotiation and expectations. The paper also discusses the main components of achieving successful scenario planning. Keywords: construction innovation, decision making, scenario planning
1 INTRODUCTION Planning of both traditional and innovative projects is an essential requirement for project success. The purpose of planning is to anticipate the future and determine how this is to be achieved. Projects that have clearly defined end-objectives and use traditional construction processes are relatively easy to plan and control. The path to the final product can be completed for the whole project and the sequential processes to perform these paths can also be identified. Innovation in construction is the process through which new ideas turn into new components of constructed products that have economic, functional or technological value. The new component may revolutionise the process itself resulting in traditional and accepted processes being replaced by new approaches that require deliberate and informed action and control. Characteristics of construction innovation include early problems; longer preparation time; high cost; more training and changes and deviations (Motawa et al 1998). The inherent uncertainty associated with construction innovation often requires several iterations to achieve a certain task or a group of tasks. Refinement or experimentation is often required before final product acceptance. These characteristics 172
emphasise that a formal planning process needs to be developed that simulates the implementation of construction innovation. This process of paths to achieve the end objective of innovative projects can not be put in the same sequence as traditional projects. Technological innovations in construction have been treated as case studies within this research to identify problems that are normally considered with innovation implementation. The progress of innovative projects has been shown to be often delayed due to changes or defective results for the proposed implementation. Several options are often available in any innovative project which should be compared at the early stage of the projects (Cushman, 1992). Successful innovations are often built on the frequent interaction (formal and/or informal) between individuals at various levels of construction organisation. In many cases this interaction should expand to outside elements such as: suppliers, regulatory agencies (Nam, 1991 and Arditi, 1997). Although several models have been developed to help organisations select between alternatives, where the innovative approach can be compared with traditional approaches such as: Lutz (1990), Skibniewski and Chao (1992), Ioannou (1993), AbouRizk et al (1994), Chao and Skibniewski (1995), and Trinh and Sharif (1996), these models have not considered innovation as a dynamic process and have not dealt with the implementation phase from the planning view. The model proposed in this research targets simulating this implementation phase. The above characteristics of construction innovation can be dealt using scenario planning technique. This paper presents the concept of scenario planning. The proposed model for implementing innovation that adopts this concept has also been illustrated. Finally, the process of achieving scenario planning has been demonstrated.
2 SCENARIO PLANNING TECHNIQUE Planning models based on decision theory, for any system of tasks, can be classified into deterministic, probabilistic and scenario planning models, which are shown in Figure 1 in terms of the associated degree of uncertainty.
Deterministic planning Low
Probabilistic planning Degree of uncertainty
Scenario planning High
Figure 1: Planning techniques These models split the decision task into subtasks, which can be performed independently and subsequently brought together to result in a specific target. The deterministic
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technique is the simplest one, where the system to be planned has well defined elements and correlation. The main characteristic of this model is that there are known variables or uncertainties that may affect the system. The probabilistic planning model, as described by Heijden (1994), contains the following components: • description of all options to be considered; • definition of a value yardstick to be used to express relative utility of each option; • identification of environmental events that could impact on the relative utility of the options; • full specification of a model specifying how the value yardstick varies for each option and environmental event; and • assessment of probabilities of these events. This process is highly ambitious as the problem covers the total area of possible futures, and requires comprehensive probability assessment to decide on utility of various options. The expected future involves events, variables, trends and their inter-relations. These future elements require respective probabilities and probability distributions that are conditional on earlier outcomes of other events and variables (cross-correlation). Subjective probability captures the main judgement of probabilistic planning. Scenario planning, as reported by Wright and Ayton (1994), is a decision-making approach which involves the analysis of multiple futures for problem structuring, in which assessment of probability is limited to a “yes/no” decision on the plausibility of self-contained story lines about the future. Heijden (1994) reported that it is a “hypothetical sequences of events constructed for the purpose of focusing attention on causal processes and decision points”. Scenario planning targets the environment, which contains, to some extent, predictable elements while other aspects are fundamentally unpredictable (called the uncertainties). Predictable elements arise for cause-effect reasons, including time delays; system constraints; feedback loops in the system; actor logic and motivation; the inertia of the system such as culture; and laws of nature. Uncertainties are expressed in terms of their multiple possible outcomes. In scenario planning, multiple plausible scenarios are designed to reflect the same prediction with different outcomes for uncertainties. Therefore, decision-makers should improve their capability to explore the environment, improve anticipation by widening perception, improve diagnosis by seeing more possibilities, increase scope for action by better understanding, and modify plans for the future for greater robustness (Heijden, 1994). The flexibility of scenario planning accepts and downplays decision-makers’ poor abilities to make realistic probability assessments for single events (Ferrell, 1994). Uncertainty that cannot be resolved cannot affect the consequence of a decision, therefore, subjective probability judgements and their quality, are critical to scenario planning.
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Subjective probability Subjective probability is a discipline to measure the decision-maker’s belief about an uncertain event considering his/her knowledge base at the measurement time (Wright and Ayton, 1994). Changing knowledge could change the uncertainty measure. The probability of a hypothesis is conditional on the items required to identify the relevant information to the problem at hand. The identification of an item of evidence influences the degree of belief in a hypothesis. However, there is considerable debate about the philosophical and psychological status of subjective probability, it still applies to encode the uncertainty present in the majority of decision problems where no other source of information about uncertainty is available. Judging the quality of subjective probability depends upon the decision process being comprehensive, having a sound theoretical basis and being carefully and systematically applied (French, 1988). The same reliance on the procedural guarantee of quality carries over to subjective probability within decision analysis. Concerning the theoretical basis, probability, as a mathematical construct, is well grounded. Qualitative judgements De Finetti’s axioms for qualitative probability (i.e. ‘Weak ordering’, ‘Independence of common events’, ‘Non-triviality’, ‘The reference experiment’, ‘Continuity’, ‘Equivalence of certainties’), are the bases of the subjective theory (Wright and Ayton, 1994). Qualitative judgements are tested by these axioms to qualify the investigator beliefs about their observations. For example, subjective judgements for some related events have to be applicable with each other. Verifying judgement is a process whereby the expert provides a true opinion in his/her approach to uncertainty and that these opinions are coherent, i.e. consistent with probability theory. This verification can be achieved by reformulating questions in logically equivalent ways to see if the results are consistent. In addition, asking questions about events whose probabilities can be inferred from previous answers or from relations between the targeted events (variables). Invariably, there are inconsistencies and these are brought to the attention of the expert and resolved by discussion and reconsideration (Ferrell, 1994). A sensitivity analysis must be performed to test the variations in the problem components and in the subjective probabilities. Probability judgement of a unique event assumes the application of a heuristic rule. This heuristic rule to plan implementation assumes that the probability of an event is derived from how easy it is to construct or imagine a scenario that leads to the event. This probability of events changes with time, where probability of correct action after an unaccepted trial of implementation increases for the next trial due to the learned experience and corrections. During the course of an analysis, the decision-maker may gather information that causes him to revise his/her beliefs. Different decision-makers may assign different probabilities to the same event. Analysis of the problem goes further by assessing a utility function to represent the preferences and by balancing beliefs and preferences to optimise the expected utility. It is possible to develop the approach to suggest a pattern of self-questioning that would lead to appropriate subjective values and to verify the judgement.
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According to the subjective probability approach, the starting point for the estimation is to assume that given any two events A and B, the decision maker has an inherent feeling of relative likelihood sequence of these events and can say whether: A is more likely than B; A is equally likely as B when he is indifferent; or A is less likely than B. The decision-maker does not say how much more likely one event is than another, only ranks them in order of the perception of outcome. Based upon this, appropriate relations can be used to build a probability function and ultimately a decision utility. The probability that attached to the possible scenarios provides a picture that relates the discussion on the most probable scenarios to the least probable ones.
3 THE PROPOSED MODEL The proposed model for simulating the implementation of construction innovation is not just a decision tool to select among alternatives where an innovation approach may be adopted, but it also provides a planning process based on causal extrapolation of the current state of affairs. This process understands the utility of the scenario-planning approach. An innovative project can be subdivided into sets of planned activities to perform an innovation stage with uncertain description of the stage’s product towards achieving the final product. After each set of activities, there is a decision node at which an analysis for the implemented work and evaluation for the performance is required. This may explain the new types of construction activity characteristics (i.e. experimentation, iteration and refinement) which have been approved by several innovative projects. Modelling innovation implementation involves deciding whether or not to accept the product of the designed construction process. If the process is faulty, it should be rejected and vice versa. Reasonably reliable performance indicators should be employed to help ascertain the process’s condition, as shown in Figure 2, where influence information indicates what affects the process condition if this innovative process, is adopted.
Influence information
Process condition
Performance indicators
Figure 2: Phase of innovation implementation The direction of the arrow indicates the direction of influence and timing. The transition within this phase is conditional on the actual status of the process. The order in which events unfold is opposite to the direction of influence. Even though ‘process condition’ influences performance, the outcomes of the measurement are known before the true process condition is determined. In this situation, the timing of the nodes should be
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opposite to the indicated arrow direction. Therefore, a decision node will be added based on the measurement results, which is called ‘Analysis’. The arrow from ‘Performance Indicators’ to ‘Analysis’ specifies that the measurement result is known before the decision is made and the true condition of the process is learned, Figure 3. The result of the decision node ‘Analysis’ might recommend a return to any of the preceding nodes; to adjust the performance indicators measurements; to modify the process condition; to check the information, or to accept the process condition as previously planned and proceed to the next implementation phase. check
modify
Influence information
Process condition
adjust
Performance indicators
Analysis
access
Figure 3: Phase of innovation implementation According to this decision analysis, the implementation problem at this stage can be represented as one state that has two main outcomes: rejection or acceptance. Rejection outcome returns the implementation of the project to the same state while acceptance moves the project to the next state. Implementation of innovation can be a recursivelydefined system with a finite number of states. Modelling these states over time can simulate the real development of innovation implementation. A directed graph called a ‘state-transition diagram’ can describe this type of modelling as shown in Figure 4. The arrows represent transitions. As time progresses, transitions take place from one state to the next. An innovation process, which is in the state I at stage N of implementation, may be in either the same state or in state II at stage N+1. Arrows that are drawn to and from the same state represent the possibility of remaining in that state for the succeeding stage. The transition can be represented by transition probabilities Pij from state i to state j. The summation of transition probabilities and the remaining probabilities in the same state, at a particular stage must be 1.0. Simulating these transition probabilities may remain the same for the model duration, as shown in Figure 4, or may have a probability distribution. To complete this model, the question ‘what is the value of being in a particular state at a particular time?’ should be answered. For this purpose, ‘value’ can be broadly defined as the cost or the time spent in each state, or any other measures of effectiveness. The implementation of innovation, as described above, can be interpreted by the relationships between an evidence and a hypothesis. The evidence is usually the result of some tests or forecasts, and the hypothesis concerns the presence or absence of a specific underlying condition. The probability concept can, therefore, simulate innovation implementation.
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Pij 1-Pij state I
state II
Figure 4: State-transition diagram The scenario-planning concept allows decision-makers to simulate the outcomes of innovation implementation based on these decision probabilities. A decision probability represents the probability of the hypothesis being true or false given certain evidence. Scenario planning can not be performed unless all involved parts contribute in innovation implementation. Therefore, scenario generation in an organisational context is targeted to achieve this aim. The difference between the organisational decision-making and the individual decision-making comes from the requirement of a degree of consensus among a group of people or stakeholders. A process of building enough consensuses is required to compromise on values, expectations and options.
4 SCENARIO PLANNING PROCESS Achieving scenario planning requires establishing of certain steps. As shown in Figure 5, the first step is the identification of the involved groups. The second step is the constitution of a shared mental model in these groups and the third step is the simulation of the mental model in the group for inferences. Step I Scenario planning process for decision problem in an organisational context should firstly specify the involved groups for the proposed innovations (Figure 5). These groups should have the capability and the responsibility to specify the work scope, the resources estimate and the schedule. These groups may include departments, employees, managers and technicians, as internal groups to this organisation. Code committee, owners, suppliers and communities are considered as external groups. Step II The second step is the development of a shared mental model in the group. Jungermann and Thuring (1987) and Kraft (1997) emphasise the importance of activation of problem knowledge in the organisational group. The interview/feedback process is a rational way to achieve this. Model building is initiated by identification of issues that will form the scenario process and link the surfaced concepts. Managers of consensus plans should test the validity of the innovative idea and the potential application range. New ideas should serve human needs and have acceptable planning functions as good as, or better than, other available alternatives. This phase should evaluate the availability of the required facilities, the expected changes for the management system and the degree to which overall strategic objectives will be achieved. It is also important to assess the impact of indirect fundamental research, expected lost ideas and efforts due to filtration of ideas. Replacing 178
existing technology with new or using both concurrently should be considered. Providing detailed descriptions of the development process to those involved and feedback from them are important. The consensus process should consider organisational structures and features that affect innovation.
Identifying the consensus groups
Step I
Step II
Departments Employees Managers Technicians
Internal groups
External groups
Problem knowledge
Code committee Owners Suppliers Community
Sharing of groups in idea development
Simulation for inferences Step III Selection from inferences
Figure 5: Scenario planning process Hodgson (1992) describes a manual technique to assist group belief that concepts are clustered in an intuitively comfortable way. Eden (1989) described a computer-facilitated mapping technique to identify linkage and concepts, resulting in an overall cognitive map that contain elements of the mental models through which the scenarios will be constructed. Many of the connections identified during the process are based on cues for causality between variables. These connections include: two variables always changing together, causes preceding effects, regular close conjunction of events, explanation by analogy and metaphor, and lack of alternative explanations. Jungermann and Thuring (1987) argued that the sense of causal connections will be enhanced by simplicity, avoidance of long causal chains and relatively strong explanatory power associated with uncommon variables. This shared mental model can be developed in computerised models and in “if-then” rules (Heijden, 1994).
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Step III The third step of the scenario planning process is the simulation of the mental model in the group for inferences. The resulting scenarios are described by a set of input variables and events that are evaluated by the degree of importance and the degree of predictability. Importance can be dealt with by assessing what really makes a difference for the studied scenarios. Degree of predictability and ranking of variables are often subjective judgements. Splitting and combining variables may arise during this step. A combination of dependent and independent variables can be included. Finally, the selection from inferences deciding on the implementation scenario of this innovation can be declared at this step. Variables and/or events in the most important and least predictable areas drive the selected scenario. Two scenarios can be adopted when there is one variable or event that is important and more unpredictable than any other to represent the range of outcomes for this variable. If more than one variable seems to have equal importance and/or predictability, the modelling moves further into sophisticated solution and the interdependence between variables is subjected to increase. Very often, in the case of high uncertainty, no single scenario gives the optimum solution. Usually, positive and negative outcomes over the full range of scenarios result. The uncertainty should be assessed over all outcomes of all scenarios by rating each option with its outcome and by considering strategic options.
5 CONCLUSION The characteristics of construction innovation emphasise that traditional planning processes need to be developed to support more effectively the implementation progress of innovative projects. The proposed modelling technique should simulate the nature of experimentation, iteration and refinement activities considering the ‘influence information’ affecting these projects and the ‘performance indicators’ to assess the implementation process of innovation. This paper has presented a decision support technique that simulates this process using the scenario planning based on quality subjective probability judgements. This technique can deal with the various uncertain outcomes inherent in innovative projects. Scenario planning helps to define all situations of a particular innovation and improves the ability to causally extrapolate theory into uncertain events. The proposed planning model for the implementation phase can fulfil the gap of fostering innovation in construction where the most important characteristics of construction innovation, high level of uncertainty and iterative nature of its activities, can be simulated and monitored.
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Lutz, J.D., Chang, L., and Napier, T.R., 1990. Evaluation of new building technology. J. Constr. Engrg. and Mgmt. ASCE, 116 (2), pp. 281-299. Motawa, I. A., Price, A.D.F. and Sher, W., 1998. The introduction and management of innovative construction processes and products. Proceeding of 14th ARCOM Conference, pp. 672-682. Nam, C. H., Gasiorowski, J. G. and Tatum, C. B., 1991. Microlevel study of integration in high-strength concrete innovation. J. Constr. Engrg. and Mgmt. ASCE, 117(2). Skibniewski, M. J. and Chao, L., 1992. Evaluation of advanced construction technology with AHP method. J. Constr. Engrg. and Mgmt. ASCE, 118 (3), pp. 577-593. Trinh, T.T.P. and Sharif, N., 1996. Assessing construction technology by integrating constructed product and construction process complexities: a case study of embankment dams in Thailand. J. Constr. Mgmt. and Econ., 14, pp. 467-484. Wright, G. and Ayton, P., 1994. Subjective Probability. John Wiley & Sons Ltd.
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