West Point

1. TST Real Estate – West Point

Case Study Overview

The objectives of this 1991 case study are to: (1) describe the inspirational moment for THINKSheet and the 1PAGER, (2) provide examples of our metric modules that combine the language of the market with our proprietary ProLingua Algorithms to picture thought processes, and (3) show how Mr. Stahl, our co-founder and CEO, and his privatization team would have used THINKSheet to accelerate the educational and decision process for our West Point client, thus increasing the odds of a successful project.

See TST Terminology site for definitions and examples of the italicized THINKSheet terms.

Site Content

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THINKSheet Genesis

“Put it on one wall so we can all understand it!”

Col. Richard Ashley – West Point Privatization Manager

West Point Main Campus

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West Point Privatization Program

Program Overview

The West Point Privatization Program was a 1991 program to convert West Point’s surplus assets to cash to pay for a new $30 million science building. The Defense Department (DOD) did not have funds available to pay for the building due to the Persian Gulf War and decided to experiment with a process it had minimal expertise in understanding and implementing.

Time & Budget Pressures

The program was subject to intense pressure because (1) West Point had no experience in real estate development and finance, thus requiring an extensive educational process, (2) the project required the reconciliation of the interests of diverse parties with competing agendas, and (3) decision-making speed was paramount due to a low planning budget.

The Eureka Moment!

After Mr. Stahl and his team made a presentation to 200+ people in the General Maxwell Taylor conference room to explain our reasoning for ranking the assets to privatize, Col. Richard Ashley, the West Point project manager, asked Mr. Stahl to use one of the military’s best practices – i.e., to:

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Teaming Up with Dr. Freedman

Mr. Stahl immediately recognized how transformative it would be to display the most important reasons for complex decisions in easy-to-understand language in a dynamic model on one page. He then teamed up with Dr. Roy Freedman, who had created a natural language-based program for decision-making. Together they created the tool that enables professionals to quantify and visualize their reasoning on one dynamic page called the THINKSheet 1PAGER.

The Key to Success: Education & Decision Speed

Assets

West Point owned assets with great value to the private sector, including the ones below:

  • 30,000 Acres of Training Land
  • Hotel Thayer
  • Michie Football Stadium
  • Eisenhower Center
  • Stewart Int’l Airport Acreage
  • Orange Country Drop Zone
  • Orange Country Land.

Interest Groups

However, the commercial use of West Point’s assets was highly controversial. The interest groups needing to be reconciled for a successful program included:

  • West Point Administration & DOD
  • DOD Lawyers
  • Hudson Riverkeeper (+ RFK, Jr.)
  • West Point Alumni
  • Orange County Gov’t & Residents
  • Tenants to pay the rents
  • Investors to finance the projects.

THINKSheet Methodology

THINKSheet modeling is a methodology coupled with a toolset to implement it.  See THINKSheet Methodology for how you do a self analysis to identify the best ways you think and communicate and then quantify them in TST models.  Our West Point model examples are a result of that process.

West Point Metrics Modules

The Stahl team included Mr. Stahl’s company, Privatization International, Inc., a real estate developer, a real estate consultant, a civil engineering company, and a large investment bank.  The Metric Module Components included the metrics, the language of the market for each metric, and a ProLingua Algorithm to quantify and scale that language.  The input source was the Stahl team’s judgments for the Ideal Project and Interest Group metric, a TST-automated 4-metric sub-model for the Financials metric, and a TST-automated synthesis of all of the model elements into an easy-to-understand story and ranking.

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 Ideal Project Metric Module

Best Analysis & Language

What was the best way to compare and quantify the projects in terms of the ideal project based on their unique features?  We obviously couldn’t use a list of pre-set words to describe unique features. We needed to communicate both the unique features and quantify their impact relative to the group.

As we often do, we used George A. Miller’s “Rule of Seven” as the benchmark.   The Rule of Seven posits that the maximum number of chunks of information people can hold in memory is seven plus or minus 2.

You will find the Rule of Seven being used for the number of comparative gradations in a high percentage of our myriad language types and QL algorithms.

The Infinite Algorithm

We chose the Infinite Algorithm because it allowed our team to use any language we could fit into small spreadsheet cells in column format for each project.   We call this language cellular language.  We profiled the language from best to worst using symbols ranging from Best (+++) to Worst ( – – -) in 7 equal gradations as illustrated below:WestPointIdealProjectsProfiles2

We typed in a judgment for each project and the algorithm quantified each judgment and added the appropriate symbol so audiences would understand the degree to which each judgment was positive or negative.

Ideal Project Analysis

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We ranked the 30,000 Training Area Acres first (+++).  Large parcels could be carved out without impacting the cadets’ training.  The surplus acres could be developed into a large-scale, multi-use, multi-year project and generate a large portion of the $30 million for the new science building.

STAS (+++) could generate a substantial percentage of the remaining funds due to its commercial location adjacent to Stewart International Airport.  The Drop Zone (++) could have been developed into a residential community given its vicinity to established residential neighborhoods and commercial amenities.

The Other Projects

We believed the Hotel Thayer would generate interest from hotel operators, but with the condition of some additional developments along the Hudson River, which would have been objectional to the Hudson Riverkeeper, backed by Robert F. Kennedy, Jr.  The Eisenhower Center and Michie Stadium were too small to make a meaningful contribution to the science building fund.  The Orange County Land projects would have been commercially viable; however, we believed the opposition of the neighboring communities, particularly hunters, would prevent us getting the necessary entitlements.

Interest Groups Metric Module

Best Analysis & Language

What were the best language and algorithm to compare and quantify the projects in terms of the likelihood of getting the approvals from the key interest groups?  Also, how could we fit multiple interest groups in one small column to avoid running out of space to keep the analysis on one page?

Our solution was to use letter abbreviations and acronyms to represent the interest groups.

Multi-Factor Algorithm

We chose the Multi-Factor Algorithm because it covers multiple factors in a small column width using letter abbreviations and acronyms.  We ranked the Department of Defense (DOD), the Market (M) and Investors (I) in order of importance as DOD, then M and then I.

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Interest Group Analysis

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The two projects with the best chance for approvals by all three groups were the Training Area and the Orange Country Land.

The problem was STAS, the project with the second most potential.  Even though we believed there would be strong market demand and readily available financing and the West Point Superintendent was willing to privatize it, the Superintendent believed the Pentagon would not approve it.

We had the same problem with the Drop Zone Land and the Hotel Thayer. Both projects were feasible but would be objectionable to influential West Point constituents, including the Administration, alumni, and/or the Pentagon.

Financials Metric Module

Best Analysis & Language

What were the best language and algorithm to compare and quantify the financials for the projects, given that we had a sub-model with 3 financial metrics that generated a set of numerical scores as the answer?

We couldn’t use the numbers for the metrics because each metric had a set of values with different ranges.  How could we take those scores and translate them into words to best communicate the results – applying the Rule of Seven?

Range Algorithm

We chose the Range Algorithm to compare the financial returns. We created a list of words that best fit the ranges of scores and the Range Algorithm automatically matched the scores with the words. The best profile was Outstanding; median profile – average: and worst profile – Unfeasable – in 7 unequal gradations.
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Financials Analysis

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The Training Area was the only project with Outstanding financials.  This was due to the large acreage, their location in the vicinity of established communities, and the ability to create a multi-year, mixed use development plan.

STAS’ financials were very strong due to the projected expansion of Stewart Airport as a major airport hub serving the Hudson Valley. The expansion would lead to facilities for aircraft maintenance, car rentals and freight shipping, plus hotels and restaurants.  Others were unfeasable in large part due to our belief that the entitlements process would not result in the projects we envisioned.

TST Synthesis on 1PAGER

The final step was how to synthesize the metrics and assumptions into a 1PAGER that (1) communicated our thought process in the easiest to understand way and (2) enabled us to collaborate with West Point in the most efficient way.  This required us to assign weights to the metrics and look at the model as a whole and determine whether all of the elements fit together as a story.  This included determining whether the mix of language and ProLingua Algorithms worked together in an effective way.

We wanted to identify the project that could generate the highest percentage of proceeds for the science building fund.  We thus assigned the highest weight of 50% to the Ideal Project and assigned equal weights of 25% to the Interest Groups and Financials.  THINKSheet automated the model below:

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Visualizing Our Thought Process

Audiences interpret the TST colors and logic at different speeds.  There is almost instantaneous recognition of the TST colors patterns.  The West Point team could immediately see that the reason the Training Area was first at 100% was because it had 3 greens.  They could also see that STAS (69%) and Drop Zone Land (43%) had the same color patterns but that STAS was 26% better by looking at their scores.

One nuance required explanation. Why were Orange County Land and the Eisenhower Center lower than Michie Stadium with two reds.  The reason was that we had set up any unfeasible projects as a deal killer.  THINKSheet automatically assigned a score of -100% for all unfeasible projects.

Time Kills Deals / THINKSheet Accelerates Them

“Time kills deals” was true for the Privatization Program due to the limited budget for such a complex endeavor with no DOD precedent.  The actual conference involved a lengthy Q&A session with the West Point community.  Although highly-educated and intelligent and used to “outside the box” scenario analysis for war planning, the West Pointers had minimal financial backgrounds.  As a consequence, Mr. Stahl’s team was unable to keep the  Q&A focused on the key issues, making the educational process of insufficient value to provide the West Point community with the educational foundation it needed to make the best decisions quickly.

If the team had THINKSheet, we could have used the TST framework to focus the audience on the most important issues at the outset and use TST’s ability to play what if in virtual real time to show the impact of multiple assumptions and scenarios on the project rankings.  If the educational process had taken less time, the Stahl team and West Point team would have accelerated the decision process and, possibly, provided West Point with its new science building.