WEI Lab Supports the VT FSAE team

2014 Car Rendering

Blacksburg, VA. It took over 25 hours of CAD, CAM, and CNC work to manufacture five different molds that the VF FSAE team will be using to make the panels for its prototype racing car.

2014 Car Rendering

The VT FSAE has been competing for over 26 years and they finished 13th in last year competition. For 2014, the team’s goal is to quality for the International Formula SAE competition in Michigan where 120 other Universities and colleges will compete.

2014 Car Rendering2014 Car RenderingThe VT FSAE is composed of 33 students divided in seven teams including suspension, drivetrain, engine, electrical, aerodynamics, testing, and ergonomics. The final prototype car must be built under FSAE regulations and must past a through inspection before it is allowed to compete.

VT FSAE 1For 2014, the team is redirecting efforts to improve several of the car components, including the body. Hence, the team started a search to locate a large CNC equipment that could be used to cut the molds required for the body parts. The material used to cut the molds is a high density foam that is easy to manufacture. The WEI CNC equipment is just what the VT FSAE team needed and under the supervision of Dr. Henry Quesada the team quickly became familiar with the CAD/CAM software and the operation of the  CNC machine.

The way that the VT FSAE operates involves knowledge transfer from senior to freshman students, as key critical factor to be able to compete and complete the project which is very similar to the approach of WEI program at the Department of Sustainable Biomaterials.


The team structure is the following

  •   Team Leader: Vincent Sorrento
  •    Team Moderator: Dan Buckrop
  •     Team Facilitator: Nabeel Ahsan
  • Sub-team structure
  • Suspension
  •         Team Leader: Hannah Bever – Chassis
  •             Nabeel Ahsan, Taylor Turner – Uprights
  •             Alex Pape – Suspension design and geometry, springs, dampers, tires.
  •             Mike Lane – Suspension Structures
  •             Cody Kees – Bell cranks
  •             James Callaway – Steering


  •          Team Leader: Mackenzie Hoover – Brakes
  •             Alex Coyle – Rear chunk
  •             Alex Girard – Shifting, Simulation
  •             Kyle Torrico, Thomas Barfield – Rotating components
  •             Brian McNulty – Wheel inners, wheel outers
  •             Danny Whitehurst – Half shafts, tripod bearings


  •          Team Leader: Dan Buckrop – Engine airflow, Intake
  •             Clay Brubaker – Controls, tuning
  •             Johnny Noble, Carter Moore – Oil, Fuel, Cooling
  •             Mark Anton – Engine airflow, exhaust


  •          Team Leader: Bryce Crane – Telemetry, diagnostics
  •             Natan Diskin – Wiring
  •             Kori Price, Glenn Feinberg, Brian Kwan – Power stream, power budget module
  •             Tyler Diomedi – Packaging
  •             Daniel Ridenour – Graduate Assistant


  •          Team Leader: Stephen Young – Under tray, diffuser
  •             Sean Lynch, Chris van Oss – Wings
  •             James Bizjak – Structures


  •          Team Leader: Brian Oeters – Test Planning & Data Acquisition
  •             Akira Madono, Dylan Verster – Test Planning & Data Acquisition


  •          Team Leader: Rachel White – Project management, cost analysis
  •             Eric Peterka, Jeff Petrillo – Pedal box
  •             Matt Marchese, Lucas Keese – Steering wheel, seats
  •             Sam Ellis – Cost analysis, facilities planning.

If you have any more questions about student CAD/CAM/CNC projects that the Department of Sustainable Biomaterials support, please contact Dr. Henry Quesada at quesada@vt.edu.


Pricing optimization and demand management in the U.S. Hardwood Industry

Edgar Arias, PhD Candidate at VT. Email earias@vt.edu

As previous research shows, pricing is a key factor in the performance of a firm in international markets.  Nowadays, it is a common practice for companies to determine their prices based on cost plus a profit markup and/or market prices (Dolan, 2008).  The hardwood lumber industry usually works in a similar way.  The problem with these approaches is that they are usually based on arbitrary decisions and do not account for the risk of either setting prices to high –and therefore losing demand, or too low and leaving money on the table.  The goal of the next phase of my doctoral study is to test a pricing methodology based on the principles of Revenue Management, that have proved to be successful in increasing profits in other industries, and that may also be valuable in the hardwood lumber industry.  The focus of this phase will be centered in exporting firms because they have to deal with more challenges and complexities at the time of determining prices than those which focus on the domestic market.

Revenue Management (RM) is the discipline within Scientific Management that deals with pricing questions such as: “how much to ask?”, “when to drop the price (if at all)”, “what the asking price should be?”, “which offer to accept?” among others, towards maximizing profitability.  In other words, RM is concerned with demand-management decisions (Talluri & Van Ryzin, 2005).  In fact, RM is also known as demand management, yield management, pricing and revenue optimization, etc.

Above questions are rather old concerns in business, as old as the notion of free market itself.  But what is innovative about the RM approach is the application of principles and techniques original in Operations Research to find the right price for “every product, to every customer segment and through every channel” (Phillips, 2005).  It is based on the fact that markets are not perfect, and in those imperfections lies the opportunity to improve prices beyond what the market dictates, in such a way that profits are also improved (Ross, 2008).

Considering that RM literature in the forest products literature is practically inexistent, we will start by studying a fundamental element of the pricing optimization process: the Price-response (P-R) function.  The P-R function (also known as curve) establishes how the demand of a product varies as a result of a change in price.  This function (Figure 1) is seller specific –companies supplying the same product to the same market will show distinct curves, and has the properties of being: non-negative, continuous, differentiable and downward sloping.  Common Price response functions are: linear, logit, S-shaped, among others (Phillips, 2005).

sim Jan 14 2

Determining the P-R function is necessary to address the basic price optimization problem which consist on maximizing the total contribution m.  Each customer order sold at a price p and with a cost c, has a unit margin equal to p-c (Phillips, 2005).  Therefore we define total contribution as following:


In general the total contribution function (Figure 2) is concave, with an apex located at the point where the first derivate equals zero.  In other words, the point is where total contribution is maximized and the price is optimal (p*).

sim Jan 14 1

This simple model grows in complexity as we incorporate elements such as supply constraints and price differentiation by regions.  In this study, we will analyze historic sales data in order to determine optimum pricing values for each product in each geographic region, which will serve the sales and marketing groups in negotiating with customers abroad in a lumber supply-constrained scenario.  In the process, we will map the path that follows from base prices, through invoice prices to pocket prices (Marn & Rosiello, 2008) –what companies actually charge –and determine why companies sometimes make less money per order than the market.  In other words, we will look for potential leakages in revenue and opportunities to fix them.


  • Dolan, R. J. (2008). How Do You Know When the Price is Right? Harvard business review on pricing (pp. 1-26). Boston: Harvard Business School Pub.
  • Marn, M. V., & Rosiello, R. L. (2008). Managing Price, Gaining Profit Harvard business review on pricing (pp. 45-74). Boston: Harvard Business School Pub.
  • Phillips, R. L. (2005). Pricing and revenue optimization. Stanford, Calif: Stanford Business Books.
  • Ross, E. B. (2008). Making Money with Proactive Pricing Harvard business review on pricing (pp. 171-200). Boston: Harvard Business School Pub.
  • Talluri, K. T., & Van Ryzin, G. (2005). The theory and practice of revenue management (Vol. 68). New York, NY: Springer.