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Analytics Case Study #1 – New Technology (supplanting an old technology using Regression Analysis)

“ABC” firm has developed a new automated technology for the quality control of food processing, which supplants traditional, labor intensive, and time consuming technology.  The system reduces quality control screening time from 48 hours to 10 minutes, with a sensitivity of 96.5% and specificity of 96.3%. The current industry gold standard technology offers 99.8% sensitivity and 99.7% specificity, but it takes 48 hours to yield results.  Company “ABC” has raised capital based on the merits of what this technology can bring to productivity in the food processing industry.  This system can increase throughput exponentially, and thereby increase revenue for companies utilizing this system, albeit with a minimum quality control downside as compared to traditional food testing.

 

Case Questions:

1.    How can this firm define its capital requirements to launch this new technology?

2.  What are the key determinants underlying the clients’ decision to adopt new technology in favor of the old and how does this affect elasticity?

3.   What are the regulatory conditions surrounding any required certification pertaining to the introduction of the new technology and how does this affect Demand Elasticity?

 

Analytics, using Demand Elasticity to evaluate the industry’s receptivity to the benefits of this new technology, the firm can consider a more accurate calculation on revenue forecasting to deliver the ROI to its investors. The Demand Elasticity can predict the receptivity of target customers when considering supplanting an existing technology.  As an example, how elastic (receptive) would a 55 year-old quality control manager be to replace a technology if it risked failure and might result in loss of a job?  At this age, this person likely has children in college and may be looking towards retirement. What if this new product failed, leading to a recall?  The potential negative impact on the brand and immediate revenue loss could be the death knell for their company’s product and the quality controls manager’s job. What is the likelihood this manager will be receptive to your new technology even if it increases throughput by a thousand percent?  The risk associated with this condition needs to be considered within a range of weighted indices and factored into the Demand Elasticity model.

Additionally, “ABC” company will likely need a longer runway and considerably more time to pretest this new technology in the field, given what is at stake for the target customer; the Quality Control Manager.  This could mean an additional year to launch and penetrate a market.  Thus, the timeline becomes another input variable related to the model’s effect on Demand Elasticity.  An SAS regression analysis could be performed by plugging in a range of weighted variables to arrive at the Demand Elasticity, as well as the probability of acceptance on what would be the most likely time line.  All of these factors, in turn, influence the amount of funding that will be required for the assumed ROI for the investors.

The assumptions that go into this model would be derived from examining a combination of factors, including but not limited to the marketing plan, competitive knowledge, focus groups, field interviews, surveys, and customer demographics. Those assumptions are something only the company can support, as it relates to the accuracy of its business plan and what determinants were used to build it.  Using Regression Analysis, where “R” is 1.0, the regression would demonstrate a perfect fit for the demographic variables to the business plan assumptions for the new technology launch. Rarely if ever will any model yield an “R” value greater than .99 but the closer “R” approaches 1.0, the higher the probabilities that the variables for ABC’s model will fit with its assumptions.  The use and testing of a regression analysis, against the variables of a business plan, can often prove highly effective in assessing greater probabilities in forecasting revenues, capital requirements and time lines.

This analysis tests the assumptions associated with conventional financial models and serves as a strong adjunct offering enhanced assurances to the predictability of the business plan.  Indeed, a demand miscalculation could throw off the revenue trajectory for the “ABC” company.  However, using Analytics, such as Demand Elasticity, “ABC” company could define well in advance that their funding is not sufficient to bring the product to the forecasted revenues.