Operations Research & Analytics
Here we apply any of a number of powerful mathematical and statistical tools to maximize the efficiency or
profitability, or minimize the financial or other costs, of various types of operations processes; or to manage
risk; or assist with decision analysis or project management.
If you need to maximize the efficiency of a process to save time or money, maximize profit, etc., we use
powerful analytic tools to achieve the optimal outcome. Here are some examples of typical
optimization solution areas:
- Make vs. Buy Decisions
- Determining Minimum Order/Purchase Size
- Transportation Route Optimization
- Production and Inventory Planning
- Data Envelopment Analysis (e.g., determining the operating efficiencies of various units within a company,
or of various companies within an industry)
- Employee Scheduling
- Contract Awarding
- Capital Budgeting
- Nonlinear Network Flow Problems
- Facilities Location Problems
- Queueing optimization to manage customer traffic or inventory
Depending on the situation, we might employ any of a variety of analytic tools and techniques to solve such
problems. For an overview of these tools and techniques, please visit our Optimization Programming overview page. Or to see applied examples of
specific optimization techniques, visit any of these specific pages:
Life Data Analysis
We can employ powerful statistical modeling techniques to estimate and predict a wide variety of
time-to-event outcomes and similar phenomena. Here are just a few examples of the almost
limitless number of application areas:
- Medical/biological survival analysis
- Industrial engineering (production/delivery)
- Weather forecasting
- Extreme Value forecasting
- Wireless telecommunications signal degradation analysis
- Insurance claims forecasting models
For more details and an example of industrial component reliability/failure analysis, please visit our
Life Data Analysis page.
Risk Analysis via Simulation Modeling and Advanced Statistical
Unlike optimization problems, where the inputs are known, risk simulation solves problems where the exact
parameters are unknown and may fluctuate randomly within a range of uncertainty. And while there are other methods
of risk analysis, such as traditional Best-Case/Worst-Case and What-If analysis, Simulation Modeling is far
superior because it uses powerful mathematical and statistical processes instead of relying heavily on intuition or
We employ sophisticated modeling techniques such as Monte
Carlo simulation analysis to
help you manage risk. And we can build models based on a wide variety of random variables. So whatever your
risk-analysis situation, we have the tools to assist you.
Here are just a few examples of how risk simulation analysis can be applied:
- For a growing company that has decided to switch from using a private health insurance company to
self-insuring its employees, risk simulation can help determine how much money should be accrued in the next
year to cover employees' health insurance claims.
- In a manufacturing environment, simulation modeling can help estimate the most cost- and time-efficient
servicing intervals for equipment.
- When considering siting facilities in a region that has a potential for natural disasters such as floods,
earthquakes or forest fires, risk analysis can assist with the decision.
- For a company that accepts customer reservations for services, risk simulation can help manage available
resources to achieve acceptable levels of under- and over-booking, thus helping to sustain both profitability
and customer satisfaction.
- Risk analysis can help management understand the financial risk associated with undertaking a particular
project, which might be affected by things such as personnel or budget problems, vendor or customer problems,
scheduling complications, etc.
- Risk modeling can assist management with inventory control, helping to determine:
- The best level of inventory to maintain
- When goods should be reordered/manufactured
- How much safety stock needs to be held in inventory
For a more in-depth discussion about risk analysis, please visit our Risk Analysis
page, which also contains links to specific examples of problems and solutions.
In addition to simulation modeling, we can also apply advanced statistical modeling techniques to solve problems
such as fraud
risk analysis, etc. (Rules-base fraud detection, while useful, has some real
limitations. Supplementing rules-based systems with advanced analytical techniques can significantly improve
detection accuracy.) Please visit our Bank Loan Credit Risk modeling
page to see a detailed example. Back to Top
We have many tools and techniques to assist with decision making. These include things such as payoff matrices
and single- and multi-stage decision trees, combined with sophisticated decision rules such as Expected Monetary
Value (EMV), Utility Functions and Multicriteria Decision Making. Our models can incorporate Bayesian analysis and
other forms of conditional probability analysis to take into account real-world uncertainties. And we can also
perform sensitivity analysis to determine how strongly decisions are impacted by changes in the model's values.
Examples of the application of decision analysis include determining:
- How large a manufacturing plant to build
- Which parcel of commercial real estate to purchase
- Whether a consulting firm should invest resources developing a complex grant proposal
- Other areas of application might include environmental remediation, healthcare management, litigation or
dispute resolution, etc.
To see a detailed discussion of decision analysis, including examples of specific techniques, please visit our
Decision Analysis page.
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Project Planning and Management
Whether your project is relatively simple and straightforward or much more complicated, we can assist with
project planning and scheduling. For simpler projects, you may be inclined to use techniques such as
CPM/PERT. But each of these approaches has limitations. So we
offer options such as CCPM (Critical Chain Project Management) and sophisticated
simulation modeling techniques for dealing with uncertainty. We can also employ powerful
optimization solutions if you need to understand the tradeoffs and overall impact of "crashing" a project by
throwing more resources at the problem to finish it faster.
To find out more, please visit our Project Management page.
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