A Three-Part Series
In the study of economics, understanding individual incentives is critical to understanding motivation, decision making, and economic outcomes. At the decision point, understanding one's "locus of control" and related psychology is the key to unlocking an individual's rational mindset. Decision support solutions can be a big help to bring together people with different loci of control, different professional judgements, and for the purpose of driving optimal and efficient decisions.
This is Part 3 of a three-part series.
(Please click the boxes for the other article parts.)
Leadership and the decision approach
Teams generally consist of leaders and participants. Those roles can be dynamic as individual leaders and participant roles may vary by the individual and the situation. For practicality, we will assume these roles are static in the short term. Team leaders must understand the locus of control perspectives of the team participants. Also, creating a sense of ownership as related to an internal locus of control is important. Team members need to have clear roles AND have the sense that they will have a positive impact on the organizational system.
Also, good decision making comes from engaging all the team members. The leaders job is to create a success environment, including:
a decision environment where the team members have a sense of agency (i.e., an optimal locus of control) provided by being involved in the decision process.
a decision process that seeks to utilize the unique judgement of the team members and associated objective information to make decisions.
a decision memory that records decisions, the individual inputs to those decisions, and the degree of variability of the decision inputs. (recall the “multimodal distribution” from Part 1).
An effective decision making approach will ultimately reduce noise and optimize the quality of the decision.
Leadership and decision support solutions
At this point in the article series, we have presented the dynamic character and challenges of our human nature in making high quality economic decisions. We appreciate group decision making is often noisy and suboptimal, owing to our naturally occurring but often hidden psychological influences. Next, we will present technology-enable, noise reducing decision support solutions and methodology. For this solution discussion, we will be primarily focused on strategic or portfolio-based investment decisions. This decision category is generally focused on companies that make decisions about how to deploy capital or related investments. This decision category is characterized by:
A fixed or finite amount of investment resources or capital to invest in projects.
A number of potential projects at various stages in their maturity cycle. That is, some potential projects maybe well known in terms of cost, benefit, risks, and impact to current state. Other potential projects may be in need of further clarity.
A relatively large group of stakeholders with various motivations and roles. This includes people that: a) provide decision judgmental input, b) provide decision objective input, c) will implement the project, d) have an invested interest in the success of the projects, or e) some or all of the above.
Financial Services companies often utilize strategic or portfolio-based investment decisions for:
Product portfolio decisions
Technology portfolio decisions
Simulation-based credit forecasting decisions
Third party supplier portfolio decisions
Acquisition or strategic investment portfolio decisions
and many others
The decision support solution methodology
Strategic investment selections are, by their very nature, a team sport. As such, the methodology must be collaborative, promote teamwork, and build consensus. It uses seven steps, as shown in the next graphic. Steps 1 and 2 are foundational. Once they are completed, the governance system and weighted scoring model(s) should be reassessed for continuous improvement on a periodic basis (perhaps annually or semi-annually). These foundational steps set the stage for the operational cycle of steps 3 through 7, which continue in an evolutionary fashion.
Step 1 - Establish a Governance System
The first order of business is to set up a governance system. This may take the form of a single investment review board (IRB), or in the case of large organizations, a multi-tier set of IRBs. For example, department-level board decisions may feed into business unit-level boards, and business unit board decisions may feed into an enterprise-level board.
Step 2 - Implement a Weighted Decision Model
Decision models are designed to help decision-makers comprehensively and consistently assess the strategic value of competing investments. For strategic investment selection, scoring models consist of a strategic goal, strategic objectives, measures (optional), and associated rating scales. Establishing the relative importance of the strategic objectives is crucial to building an effective scoring model. The weighting factors must be accurate and enjoy the consensus of the group. The Analytic Hierarchy Process (AHP) is the gold standard for performing this critical step. (6)
Step 3 - Collect and Catalog the Investment Proposals
Keeping in mind that the best ideas often come from those are closest to the “problem”, and that the diversity of proposals will be directly correlated to the diversity of those who are able to participate, the opportunity to prepare and submit an investment proposal is extended to the largest number of stakeholders that is practical for the organization.
Step 4 - Review and Score the Investment Proposals
To build the most consensus in the investment decisions, all investment proposals are evaluated against all the strategic objectives. When investment proposals are partially evaluated or rejected without an evaluation by the IRB, the degree to which the team will embrace the investment decisions is negatively impacted.
Step 5 - Analyze the Results
The facilitator-led investment scoring sessions generate a significant amount of data to analyze, requiring the generation of several charts and tables to support the analysis and facilitate the IRB’s review and decision. The analysis covers strategic business value, costs, and risks. The analysis approach enables the separation and clarification of objectives, facts, and the judgments of the team members.
Step 6 - Optimize the Allocation of Resources
Using a mathematical programming solver for optimization (i.e., an optimizer), the IRB will extend their decision-making capabilities beyond prioritization. Optimizers are in the most advanced category of data analytics tools, which is prescriptive analytics. They help answer the question: “What should we do?” They are particularly useful when making strategic investment selections because when multiple investments are being made, the purpose of the decision is not to select the most preferred investment, but rather to select the most preferred combination of investments that deliver the maximum benefit to the strategic goal (i.e., “bang for the buck”).
Step 7 - Make and Communicate the Investment Decisions
Prior to making investment decisions, a sensitivity analysis is conducted to determine the degree to which the recommendations are sensitive to slight variations in the weighting of the strategic objectives. The sensitivity analysis will increase the IRB’s confidence in their decisions.
The Key Benefits
This strategic investment decision-making methodology consistently delivers the following benefits:
Process consistency and transparency
Improved stakeholder engagement
Increased cross-functional insight
Greater consensus and buy-in
Optimized allocation of resources
Higher velocity decisions
Better return on investment
Strategically aligned investments
Justifiable decision rationale
Historical record of decisions
The Decision Support Solution Methodology was created by Definitive Business Solutions, Inc. This methodology is enabled by Definitive Pro®, a cloud-based, group decision support service that provides a collaborative process to build consensus and make complex, multi-criteria decisions in a wide range of scenarios. It uses the leading theory in multi-criterion decision making (the Analytic Hierarchy Process), which provides the ability to synthesize quantitative and qualitative factors and set priorities. It also employs a state-of-the-art mathematical programming solver to find the most favorable solution and optimize the allocation of resources. (7)
For more information, please contact Definitive Business Solutions, Inc.:
John Sammarco, President | JSammarco@definitiveinc.com
Jeff Hulett, Executive Vice President | JHulett@definitiveinc.com
Notes
(1) John Muth (1961) Rational Expectations Theory. The theory was more extensively utilized in macroeconomics by Robert Lucas.
(2) Daniel Kahneman and Amos Tversky (1979). Prospect Theory: An Analysis Of Decision Under Risk. The theory demonstrates how people are asymmetric in how they react to loss aversion. It also shows how psychological anchoring can impact this reaction. Daniel Kahneman earned a Nobel Prize as a result of this theory and work related to the integration of psychology and economics.
(3) Julian B Rotter (1966). Generalized expectancies for internal versus external control of reinforcement
(4) Daniel Kahneman, Cass Sunstein, and Olivier Sibony (2021) Noise: A Flaw in Human Judgment. The 4 listed gap types are related to noise archetypes described in Noise. They are Level Noise, Stable Pattern Noise, and Occasion Noise. When comparing bias and noise, the authors make a powerful case that noise is far more impactful and difficult to manage in the decision making process. A more fulsome explanation is provided in:
Jeffrey Hulett (2021) Good decision-making and financial services: The surprising impact of bias and noise.
(5) Richard Thaler and Cass Sunstein (2008) Nudge: Improving Decisions About Health, Wealth, and Happiness
(6) Thomas L. Saaty (1982) Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World
(7) Definitive Business Solutions, Inc. designed and integrated a bespoke solver engine, utilizing a number of solver programing code sets and data transmission capabilities, uniquely needed for the AHP and cloud environment.
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