Abstract
We introduce the conceptual and theoretical foundations of our prescriptive paradigm for robust decisions. Whereas decision-making is an event, executive decision management is a life-cycle of a complex of five spaces. The five spaces are: The Problem Space, Solution Space, Operations Space, Performance Space and the Commitment Space. Consistent with the prescriptive nature of our paradigm, we concentrate on actionable processes and procedures within each of those five spaces. The goal of our prescriptive paradigm is to enable systematic design of robust decisions. The key sociotechnical processes are robust design synthesis, Design of Experiments (DOE) using gedanken experiments, Gage R&R, making uncertainty tractable with spanning set of uncertainty regimes, and the process to represent system behavior phenomenologically.
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Appendices
Appendix 3.1 Keeney’s Techniques for Identifying Objectives
The table below is taken directly from Keeney’s (1996) article on this subject. This is not a recipe for finding the objectives for a decision problem, but it is an approach to explore the thinking of the decision maker.
Type of Objective | Questions |
|---|---|
Wish list | • What do you want? What do you value? • What should you want? |
Alternatives | • What is the perfect alternative, a terrible alternative, some reasonable alternative? • What is good about each? |
Problems and shortcomings | • What is right or wrong with your organization? • What needs fixing? |
Consequences | • What has occurred that was good or bad? What might occur that you care about? |
Different perspectives | • What are your aspirations? • What limitations are placed upon you? |
Strategic objectives | • What are your ultimate objectives? • What are your values that are absolutely fundamental? |
Generic objectives | • What objectives do you have for your customers, employees, your shareholders, yourself? • What environmental, social, economic, or health and safety objectives are important? |
Structuring objectives | • Follow means-ends relationships: why is that objective important, how can you achieve it? • Use specification: what do you mean by this objective? |
Quantifying objectives | • How would you measure achievement of this objective? • Why is objective A three times as important as objective B? |
Appendix 3.2 Smith’s Approach to Conceptualizing Objectives
The table below is an extension from Smith’s article (1989) on conceptualization of objectives. All eight conceptualizations are different types of “gaps.” To show what we mean, we restate his examples as a “gap statement.” Discovering corporate gaps is where we begin in our field experiments with our executive interviews. Simultaneously we try to learn as much as possible about the conditions and historical situations that led to these identified gaps. From this we distill corporate objectives we want to study. Then the background of the gap becomes what we call “the decision situation,” which gives the context of the corporate problem and objectives senior executives want to achieve. This is a way to frame a decision situation.
Example | Description | Conceptualization | Gap Statement |
|---|---|---|---|
“Sales are $150,000 under budget.” | Comparing existing and desired states | Gap Specification | Same |
“It‘s tough competing, given our limited experience in this market.” | Identifying factors inhibiting goal achievement | Difficulties and Constraints | “The differences between our experience and what is required are ... |
“We need to convince management that this is a profitable market.” | Stating the final ends served by a solution | Ultimate Values and Preferences | “We need to show +x% more profitability to our management.” |
“This year’s sales target of $5.2 million must be met.” | Identifying the particular goal state to be achieved | Goal State Specification | “Current sales are $x M, a shortfall of $Y M from target of $5.2 M.” |
“We have to hire more salespeople.” | Specifying how a solution might be achieved | Means and Strategies | “We are short of +xx sales people.” |
“The real problem is our ineffective promotional material.” | Identify the cause(s) of the problematic state | Causal diagnosis | “Our promotional material is ineffective in the following areas because ....” |
“Our product is 6 years old; our competitors out-spend us on advertising; etc.” | State facts and beliefs pertinent to the problem | Knowledge specification | “Our product is 6 years old; competitors out-spend us on advertising by x% per y unit sales ...; etc.” |
“Since the market isn’t growing, we’re in a zero-sum game with our competition.” | Adopting an appropriate point-of-view on the situation | Perspective | “We need to gain share of x% from our competitors ...” |
Appendix 3.3 Eight Analytic Structures
von Winterfeldt and Edwards (2007) specify eight mathematical structures to model the system behavior to predict and analyze variables that have an influence the outputs. These approaches are not limited to mathematical structures. They are also very effective in qualitative analyses as well. Our descriptions that follow are presented in this spirit.
Means-Ends Networks
This process can start at any level of a problem or opportunity, say at level n. To build the means-ends chain, ask the question: “why?” Viz. why is this objective important? Itemize the reasons and now you have the n − 1 level of the network. Next from the n level, ask the question: “how?” Namely, how will this objective be accomplished? Itemize the answers and now you have the n + 1 level of the network. Proceed iteratively, up or down or both, until you have found the appropriate level at which to address the opportunity/problem. Clearly the process can produce very complex networks.
Objectives Hierarchies
Objectives hierarchies are simple two-column tables. On the left hand column list your itemized list of objectives. On the right hand column, for each objective, list the measures to achieve the objective. For example, for the objective to: “Improve customers’ service economics”, the right hand column can show, for example, “reduce consulting fees.” Or “provide the first 50 h of consulting for free”. Complete the table and you have an objective hierarchy.
Consequence Tables
Consequence tables are also two column tables. On the left hand side list the fundamental objectives and the right hand side specify the measures. (This is almost identical to Objective hierarchies.) Complete the table in this manner and you have a consequence table.
Decision Trees
Decision trees begin with a decision node, N0, normally depicted by a square. Emanating from the N0 node are the various links identifying alternative decisions, say d1, d2, and d3, that can be made. Each of these links terminate in a chance node, normally identified by a circle. To each d1, d2, d3 link, a probability can be assigned. Links emanate from each of these circles to potential outcomes with an associated payoff. Suppose that from d1 we have 2 links to outcome o11 and o12; from d2 we have outcomes 021, 022, and 023. And from d3, we have outcome o31 and o32. The expected value of the outcome o32 is the product of the probability of d3 and payoff o32. This a schematic description of a decision-tree of 3 layers. A decision tree becomes very bushy when it has many levels.
Influence Diagrams
Influence diagrams are the inventions of Howard (2004) who coined the term “decision analysis”. An influence diagram is graphical representation of the decision in question. The diagram is represented with the following elements: decision nodes as rectangles, the outcomes and their value represented as octagons or rectangles with rounded corners, and functional arrows to show the variable-nodes on which values depend. Clearly, a functional arrow must exist between a decision (rectangle) and outcomes (octagon or rounded-corner rectangle). Using these geometric illustrations a network that represents the causal relationships of a decision can be illustrated.
Event Trees
Event trees are built from the “bottom up”. The consequences of an event are identified in a step-wise feed forward successively branching out as in the decision tree approach. Event trees are often used to determine probabilities of failures and other undesirable events. This a “bottom up” approach.
Fault Trees
This is a so-called “top down” approach. This is the opposite approach of event trees, which uses a “bottom-up approach”. The idea of a fault tree is to start with a fault. A fault can be understood as an engineering failure or a serious deleterious sociotechnical outcome. The fault tree is constructed starting with fault and identifying the reasons leading to the fault. Reasons can be conjunction or disjunction. The process proceeds iteratively down using the same logic.
Belief Networks
A belief network is a directed acyclic network/graph, with an associated set of probabilities. The graph consists of nodes and links. The nodes represent variables. Links represent causal relationships between variables. In general, a belief in a statement/hypothesis S, involving variables, that depend on some prior related knowledge K. Our belief in S, given we know something about K, forms a belief function P(S|K). Bayes theorem give us a way to determine the value of this expression. Thus associated probabilities and prior knowledge gives us a way to reason about uncertainty. Modeling a problem this way involves many nodes that are linked, forming a Bayesian Belief Network.
Appendix 3.4 Debiasing Logic
This debiasing procedure is from Lerner and Tetlock (2003).
Appendix 3.5 Examples of Engineering Applications Using DOE
Engineering problems | Reference |
|---|---|
• Chemical vapor deposition process • Tuning computing systems • Design of accelerometer • Paper feeder w/o misfeeds and multifeeds • Waste water treatment plant • Camera zoom shutter design • Capstan roller printer • Numerically controlled machine • V-process casting Al-7%Si Alloy • Development of a filter circuit • Gold plating process • Optimization of inter-cooler • Replenisher dispenser • Warfare Receiver System • Body panel thick variation • Tensile strength of air bag • Electrostatic powder coating • Chemical reaction experiment • Task efficiency • Injection molding shrinkage • Carbon electrodes study • Clutch case study • Medical serum • Multicycle chemical process • Yield of chemical process • Impeller machine for jet turbines • Medical serum | Phadke (1989) Phadke (1989) Antonsson and Otto (1995) Clausing (1994) Clemson et al. (1995) Fowlkes and Creveling (1995) Fowlkes and Creveling (1995) Wu and Wu (2000) Kumar et al. (2000) Wu and Wu (2000) Wu and Wu (2000) Taguchi et al. (2000) Taguchi et al. (2000) Taguchi et al. (2000) Roy (2001) Roy (2001) Roy (2001) Wu and Hamada (2000) Wu and Hamada (2000) Wu and Hamada (2000) Frey et al. (2003), Frey and Jugulum (2003) Frey et al. (2003), Frey and Jugulum (2003) Nalbant et al. (2007) Montgomery (2001) Montgomery (2001) Montgomery (2001) Jahanshahi et al. (2008) |
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Tang, V., Otto, K., Seering, W. (2018). Operations: Foundations and Processes. In: Executive Decision Synthesis. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-319-63026-7_3
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