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"Reshoring" requires technological innovation. Transitioning manufacturing back from lower-cost regions into higher-cost regions will require both governmental support and adaptation of efficient manufacturing methods.
First, it was trade conflict between China and the U.S., and then COVID-19 turned business and trade on its ear, with even the most commerce-friendly countries protecting their supplies of essential items for national use. With these events as backdrops, many commentators are predicting a “reshoring” of key manufacturing, as companies in crucial industries are expected to bring manufacturing and critical supply chains onshore, often with the support of national governments.
Taking pharmaceuticals as an example, the October 30, 2019 testimony of the U.S. FDA’s Dr. Janet Woodcock, the Director of the Center for Drug Evaluation and Research, is quite timely (1). In her Congressional testimony, she stated that 72% of the manufacturing facilities making active pharmaceutical ingredients (APIs) supplying the U.S. market were overseas. Of the 370 drugs marketed in the U.S. that are on the World Health Organization’s “Essential Medicines List,” only 21% of the production facilities (by number) that make them are in the U.S. Finally, API sites manufacturing so-called “medical countermeasures” against threats are woefully underrepresented in the U.S. – for example, only 11% of the facilities worldwide for manufacturing APIs to defend against biological threats and influenza.
Manufacturing for APIs has moved out of developed countries for decades, for several sound business reasons. API manufacturing is chemically intensive and therefore increasingly difficult to site due to environmental regulations. Labor cost differentials have driven particularly less-profitable generic drug manufacturing to seek lower rents. As a result, with common manufacturing methods, natural economic forces have dispersed pharmaceutical manufacturing around the globe.
Similar tales could be told of other complex and/or energy-intensive manufacturing tasks, from semiconductors to steel. In fact, according to the National Association of Manufacturers, only 11.39% of the total output of the U.S. economy is manufacturing (2). Similar statistics highlighting a trend toward lower manufacturing intensity are available for many countries in Western Europe as well.
Transitioning manufacturing back from lower-cost regions into higher-cost regions will require both governmental will and adaptation of manufacturing methods. To be cost-competitive on the world stage, this transition very well may be a golden opportunity to upgrade to advanced manufacturing methods. Optimizing manufacturing requires improvements in efficiency in both labor and resource use, minimizing waste of hazardous chemicals while maximizing plant output. Such efforts can reduce the environmental impact and the cost of the business.
Spectral sensing facilitates and supports advanced manufacturing and can be used in a variety of places throughout the manufacturing process. Supported by machine learning, spectral sensing can identify material types, derive exact concentrations, and feed into optimal control systems. Ocean Optics' founder, Mike Morris, is credited with popularizing the miniature spectrometer nearly 30 years ago; the product that Ocean Optics made ubiquitous is now widely available and is a key tool for spectral sensing.
Here are just a few of the ways that spectral sensing can be used to aid in manufacturing efficiency:
Spectral sensors can be employed to measure solids, liquids, and gases, and can be implemented either in contact with the material to be measured or in non-contact mode. For example, Raman spectroscopy may be employed to measure API concentration using “attenuated total reflectance” probes for liquids or solids, while other liquids and gases may be better measured using direct absorption, transmission, or fluorescence after light interacts with the sample. Plasma spectroscopy (LIBS) can be easily used to measure metal or geological composition, while direct spectral imaging can very rapidly detect contaminants in food or other defects on conveyors.
Machine learning and artificial intelligence are key parts of any sensing system. Most process analytical technology (PAT) sensing solutions combine real-time measurement with machine learning models to derive material type, concentration, and/or detect important parameters. Multiple measurements can feed into process models, which are used to optimize batch or continuous operational parameters, predict yield, and minimize waste.
Spectral sensing is clearly on the rise in manufacturing, and this key trend will continue for the next decade. With a global presence in North America, Europe, and Asia, the Ocean Optics commercial and engineering team has the experience to develop spectral sensing solutions for any manufacturing environment, and the reach to support them wherever your factories may be. With a full suite of hardware and software tools, let us help you build the future – wherever you call home!
(2) https://www.nam.org/state-manufacturing-data/2019-united-states-manufacturing-facts/, retrieved 5-4-2020.