This is WIP.  We will update with addt'l examples (screenshots, maybe some video, descriptions, etc.).

List of examples:

   A. Leveraging Investments in a Shared Service Environment
   B. Inventory Delivery and Management
   C. Capital Planning
   D. Revenue Projection
   E. Logistics and Service-Level Agreements for Multiple Companies
   F. What-If Scenario Analysis (Before and After "Relative" Analysis)
   G. Workforce Planning
   H. HR Strategy

A. Leveraging Investments in a Shared Service Environment

Industry: Broadcasting, Radio, Media
Functional Areas: Investments, Shared Service, Infrustructure, Systems Implementation, Mergers and Acquisitions.

The Problem:
Client wanted to capitalize on recent mergers and acquisitions.

Complexity:
How do we take advantage of economies of scale?
How do I institutionalize the new way of thinking about who we are?

Solution:
We built a management flight simulator to help decision-makers understand the options and investments that were previously unavailable.  The application was presented as a “management game” that allowed managers to run the operations, make decisions, and compare results.

B. Inventory Delivery and Management

Industry: Industrial Chemicals
Functional Areas: Inventory Management, Supply Chain, Strategic Sourcing, Vendor Management

The Problem:
Client needed to plan inventory management to support offshore operations.  Plan needed to be communicated to multiple stakeholders.

Complexity:
Operating theater was in a new location, with very little existing infrastructure.  Furthermore, operations called for unprecedented levels of volume and products/grades and source locations.

Solution:
We built a model that accounts for complicated feedback and delays in the system.  This allowed client to set optimal inventory policies.

C. Capital Planning

Industry: Higher Education
Functional Areas: Capital Planning, Financing, Planning and Budgeting, Board-level Presentation and Discussions

The Problem:
Client was considering over 30 capital projects of varying sizes and complexity.

Complexity:
This was a highly political situation.  Not all projects would be started.  Some had complex financing implications and interdependencies.

Solution:
A model that allowed quick what-if scenarios allowed effective decision-making sessions.  The recommendations showed the total sequence of projects as well as the overall financial impact.  Changes to recommendations were accommodated quickly.

D. Revenue Projections

Industry: Subscriptions, Hospitality
Functional Areas: Revenue Projections, New Business/Product Planning, Customer Experience

The Problem:
Client wanted to aggressively market an existing subscription tier.  The Board asked management to provide revenue projections.

Complexity:
This was for an subscription level that had not received much attenton.  We had lots of data and information about the buyers of such packages.  We knew we could increase revenues from new sales as well as upselling from ther tiers.  However, the board was weary of any projection that was a simple percentage increase from previous years.

Solution:
We built a system dynamic model (the dynamics of customer decisions had lots of feedback elements) with a Monte Carlo analysis.  This allowed us to help the clients understand the range of possible revenue results for the next several years.

The result of the Monte Carlo run is shown above.  The blue line represents lining up all the revenue possibilities from 10,000 "runs", starting with the highest revenue result, lined up in descending order to the lowest reveue result.

Using the sanitized numbers above, our client management was able to answer the specific questions from the board:

1. Can you reach $4,000?  (short red line near 4,000)
   Answer: Yes, but only in less than 5% of the resulting runs.  (green dotted line)
   
2. What is the 80% likelihood scenario?  (red vertical line)
   Answer: We project that in 80% of the revenue possibilities will be above $1,450.  (red horizntal line)
   

Clients used this logic to project that they could achieve $1,450.  Then they used the model to ask a different question: how can we improve our revenues?  The model was then used to calculate the number of additonal resources that would yield improving revenues and at what point the cost of the additional resource would have dimishing marginal returns.

E. Logistics and Service-Level Agreements for Multiple Companies

Industry: Offshore Logistics
Functional Areas: Service-Level Agreements, Supply Chain, Contract Management

The Problem:
Two companies were contemplating a long term supply arrangement: one buyer, one supplier.  The buyer in this case owned a number of offshore energy production and drilling platforms.  The supplier serviced these platforms by shuttling marine vessels from the shorebase to the facility carrying diesel fuel, potable water, drilling mud, food…a myriad of supplies.

Complexity:
The buyer could have engaged in the age-old “beat the supplier down to get the best cost” posture.  Instead, both firms recognized that value could be created by each firm coming to the table with ideas to lower the overall cost of supply.  They did this using a simulation model.

Solution:
With the model, the companies “designed” a logistics system between them on paper, and tested this design under numerous scenarios.  The supplier made suggestions that actually reduced the fleet size by two vessels.  Why do such a thing?  Because the firms had agreed to a gainsharing agreement whereby each firm kept 50% of the savings from the older system that was to be replaced.

The logistics system is now 6 years old, and has weathered hurricanes, a trebling of crude prices, labor shortages, and drastic changes to production infrastructure.  Both firms hold this operation up as a best-practice in the industry, unaccustomed to such partnership.

F. What-If Scenario Analysis (Before and After "Relative" Analysis)

Industry: Transportation Scheduling
Functional Areas: Multi-Company Arrangement, Logistics, Infrastructure Investment, Contract Negotiations

The Problem:
A consortium of operators in the Gulf of Mexico saw an opportunity to coordinate scheduling of flights for helicopters.

Complexity:
Many operators had objections and constraints.  For example, some could not fly at night, while some always required two pilots.  With so many constraints, will the opportunity actually exist?

Solution:
Our model allowed us to compare the two worlds.  The analysis showed that even with all the constraints, an opportunity existed to save money.  The model and the resulting analysis was used to justify the investment was to be made by members of the consortium.

G. Workforce Planning

Industry: Utilities
Functional Areas: HR, Workforce Planning, Development, Recruiting Programs

The Problem:
A utilities client wanted to understand and make policy decisions around a key position in the workforce.

Complexity:
This position was expected to be high demand; however, fewer workers were acquiring associated skills. 

Solution:
By using a model that simulated the workforce, the client was able to run experiments and “what-if” scenarios.  They were able to balance multiple objectives (service quality, overall costs, risks, overall image, etc.).

H. HR Strategy

Industry: Federal Government, Non-Profit
Functional Areas: HR, Training & Development, Recruiting, Workforce Planning

The Problem:
The US Navy wanted to “fix” many aspects of the entire “recruit to retire” process..

Complexity:
Over the years, many “fixes” had been applied.  Most addressed isolated issues that eventually led to other sub-optimal impacts elsewhere in the organization (many unforseen consequences).

Solution:
Using Systems Thinking, we built a qualitative model to understand some of the delays, feedback and unintended effects.  We then built a quantitative model that allowed us to “graph out” the impact of the policy changes.  This allowed us to identify appropriate intervention areas.

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