Just as stores are discounting once scarce bottles of hand sanitizer, many people have more than enough rapid tests filling up their drawers. That includes the free tests shipped directly to homes by the U.S. government. And now insurance companies will pay for 8 tests per person per month.
Supply chain difficulties have become standard for nearly all businesses during the pandemic. But one of the bigger issues with the COVID-19 testing supply chain is determining demand, especially for at-home test makers, writes Supply Chain Dive (April 14, 2022). Manufacturers would shutter factories, only to scramble to reopen them when new waves of the virus pushed up demand.
Longhorn Vaccines and Diagnostics received an FDA request to massively scale up demand for at-home tests in 2020. The company historically produced at most 100,000 units a year, and the FDA was requesting 3 million units a week. Longhorn was able to make 50 million units that year after ordering large quantities of chemicals from China and working with tube manufacturers to scale up production. In early 2021, the company decided to stock up even more on chemicals to ensure it could last through 2 years of peak COVID. Storage costs were high, but the company couldn’t afford to run short on the chemicals. Then, in February 2021, testing slowed. One week Longhorn was selling 10 million units, and the next week, people were canceling orders. So when orders stopped, the supply chain was already full of tests.
At-home testing is challenging because it’s essentially a new market, as we note in Chapter 4, and it’s hard to find similar products for comparison in forecasting. There is no historical data as a comparison. There is also volatility over what is driving demand. It can be the community infection rate, office or school requirements, requirements for travel, policy issues, port availability, and cost. Predictive analytics (our topic of Module G) can be used to anticipate demand, but these analytics must be taken with a grain of salt due to the volatility.
There are costs of having too many and too few products: If a company estimates a demand for 1 million tests per week, does it expect variability between 900,000 and 1.1 million, or between 500,000 and 1.5 million? That affects production and logistics, with supply procurement, warehousing, and trucking reservations.
Classroom discussion questions:
- What forecasting techniques in Chapter 4 might be used in this case?
- What are the 3 categories of analytics noted in Module G?