Manufacturing grew America into the economic powerhouse it is today. From the mills in Lawrence to Ford's Model-T lines, America made exceptional goods, and they were in great demand. Out of the chimneys and grease arose the strongest middle class any country has ever seen. This was the golden era of American manufacturing and American life.
The 70's marked the beginning of the greatest transfer of industrial knowledge since Samuel Slater brought English textile mill designs to America. For decades thereafter, developing countries in other parts of the world eagerly accepted American offshoring and began building manufacturing systems at scale, iterating and improving upon the best practices we first pioneered at home.
Today, those countries stand as some of the greatest competitive threats to American industry. They build more, faster, and for cheaper. On the other hand, as America painfully learned during COVID, we struggle to make even the most basic critical goods.
As one of my mentors put it:
We need young people to rebuild America's industrial base if we are to survive Rick. This country is the bastion of freedom in the world and we have degraded our industrial capacity to defend ourselves.
Freedom isn't free. Americans will die to protect it. It's up to you and your cohort to build our capacity to support the Americans who are in the fight today and those who will come after.
We are doing our part. We need you and your cohort in this fight!!
— One of my mentors
Today, American factories are plagued by inefficiencies, driven in part by low visibility at the system level, slow time-to-reaction, and sub-optimal decision-making. When operating conditions shift—a machine goes down, a raw material is at risk, a customer puts in a last-minute rush order—factories are slow to react and spend an enormous amount of wasted time "firefighting" these issues.
From factory to factory, the problems look different but their underlying sentiments are the same: factories are chaotic systems with millions of actions, objects, and constraints, and operating conditions can change at the drop of a dime.
Reconciling these two truths to make decisions has traditionally required a feat of wrangling tribal knowledge, disparate data systems, and gut reactions. Out of all this chaos, humans are tasked with making the correct choices at every moment, as quickly as possible. When these decisions are wrong, factories risk millions in downtime, wasted energy, and customer dissatisfaction. How can factory operators make the correct decisions with confidence and clarity?
Scientific Management
One approach has been through Scientific Management (SM), first pioneered by Frederick Taylor. The following are excerpts from Factory Physics by Wallace Hopp and Mark Spearkman:
"Scientific management (SM) made the modern discipline of operations management (OM) possible. Not only did SM establish management as a discipline worthy of study, but also it placed a premium on quantitative precision that made mathematics a management tool for the first time. Taylor's primitive work formulas were the precursors to a host of mathematical models designed to assist decision making at all levels of plant design and control. These models became standard subjects in business and engineering curricula, and entire academic research disciplines sprang up around various OM problem areas, including inventory control, scheduling, capacity planning, forecasting, quality control, and equipment maintenance."
Yet as scientific management began to bloom in universities, later evolving into operations management/industrial engineering, the increasing need for managers placed the infantile subject under academic scrutiny. Already an outlier of traditional subjects, universities began hiring more and more strict academics to teach it instead of industry experts. Thus began the alienation of operations research from the reality of industry applications:
"By the 1990s it was apparent that business schools and corporations had swung far apart, with industry naively relying on glib buzzword approaches and academia leaning too far toward specialized research and imitative teaching. It remains to be seen whether this gap can be closed."
Today, very few factories we've seen actually use quantitative principles in their daily operations. At best there are a few simple regressions to predict demand, or a series of Excel spreadsheets for calculating inventory. Some ERPs and other industrial softwares use simplistic algorithms for FIFO scheduling but take an extraordinary amount of time and money to adapt when systems and products grow. At the end of the day, strict operations research, which is a very well-defined discipline, rarely makes it to the floor. There is an unprecedented opportunity to transfer this knowledge back into the hands of factories to unlock material gains.
The ProDex Vision
ProDex Labs was founded under the belief that factories can become the most beautifully efficient systems in the world. We seek to bridge the gap between operations research theory and actual factory operations as quickly as possible. Imagine: every person, machine, material, and other object perfectly orchestrated as one congruent manufacturing system. Whether it's supply & demand shocks or labor allocations, our software uses well-defined algorithms to continuously optimize for the best decisions. When things go wrong, operators are able to quickly pivot, getting their production system back on track in seconds.
This is a world that is already shaping up overseas. Our strength lies in intellectual curiosity, humility, rigor, and resolve. It's time for America to step into the future of manufacturing, faster and better.