Feb_March_AMP_Digital

A D V A N C E D M A T E R I A L S & P R O C E S S E S | F E B R U A R Y / M A R C H 2 0 2 0 2 5 Fig. 1 — The four industrial revolutions reflect a change from use of simple machines and hand tools to machines that learn and optimize processes without manual intervention. INDUSTRY 4.0 MEETS THE STAMPING LINE Ford Motor Company’s stamping division looks to leap into Industry 4.0 the same way Henry Ford led the transformation from Industry 1.0 to 2.0. Jason Ryska Ford Motor Company, Dearborn, Michigan T he personal transportation in- dustry has undergone essentially four major transformations since the period recognized as the start of industrialization. The first transforma- tion, Industry 1.0, is the start of mod- ern manufacturing, roughly 1760-1840, when hand tools were replaced with machines, and horse-drawn transporta- tion was ubiquitous (Fig. 1). Ford Motor Company has a lot of pride in the role Henry Ford played in taking vehicle pro- duction from Industry 1.0 to Industry 2.0 with the mechanized assembly line. The 20th century gave way to Industry 3.0, including the rise of electronics, computer technology, and automa- tion in design, analysis, and production streams. Ford Motor Company intends to have a similar revolutionary pres- ence in the move to Industry 4.0, where machine learning and digital transfor- mation drives innovation. The concept of this fourth industri- al revolution has seen use and is often associated with the customer-specific advertising that results from an analy- sis of data streams created by purchas- es, browsing history, and trends in the customer’s life. This is what the major- ity of consumer-focused companies are doing with data: using 4.0-based ana- lytics to understand customers at a lev- el not previously imagined. Artificial Intelligence (AI), while largely used as a buzzword with vague concepts of de- liverables, has grown into a standalone industry with more tools and more agil- ity available every day; putting massive scales of data organization and analysis at the user’s fingertips. REAL-TIME PROCESS OPTIMIZATION Consumer driven industries, es- pecially ones with high levels of capital investment to make their products, are evolving in how they consider the role of data in production; running the busi- ness through data analytics and phys- ics-based models to refine and adapt to product demands. In many production processes, data analytics provides the agility to keep up with market trends and technology advancements. An exception to this trend is auto- motive production, a multi-billion dol- lar industry that is underutilizing data collection and underestimating the po- tential improvement that may come from understanding the data being col- lected. The industry leaders in automo- tive, the companies making millions of vehicles per year, have very limited