Additive manufacturing (AM), otherwise referred to as 3D printing, has long been one of those technologies that seems to be just beyond our grasp. By many accounts, this will soon cease to be the case. Gartner estimates that by 2021, 20% of the world’s top consumer goods manufacturers will use 3D printing to produce custom products. Some businesses are already establishing internal start-ups with the intention of refining 3D printing techniques and best practices, and as the process gains speed and production quality it will soon become a viable method for mass production and a disruptive force across the manufacturing sector. 

If you had walked onto a factory floor during the second or third industrial revolution, it would have been immediately obvious what was so modern about what you were witnessing. You would have seen raw parts being turned into complex products on a moving assembly line, or newly automated processes making use of modern industrial machinery and early computer networks. In the world of Industry 4.0, the so-called “fourth industrial revolution,” the differences in appearance might be more subtle. You might still see a mix of manual labor and automated, computerized systems carrying out various production tasks, while many of important innovation brought about by Industry 4.0 might remain invisible to you. You might even be prompted to ask, “what’s so modern about modern manufacturing?”

Although automotive manufacturers have been hearing for years that Big Data is the next big thing, studies often show that executives, not just in automotive but across many different industries, fear that their organizations aren’t ready to take advantage of the new advancements in analytics. Big Data analytics can and will be a huge value-added proposition for companies hoping to stay competitive in the world of Industry 4.0, but it’s true that reaping the benefits of new technological insights often requires significant changes in workflows and IT infrastructure. Luckily, these changes are often not as daunting as they first appear. Here are a few suggestions for getting the most out of your advanced analytics.

Murphy’s Law states that whatever can go wrong, will go wrong—and nowhere is that more true than in the world of global supply chain management. Risk is simply a fact of life in almost all business spheres, but automotive industry manufacturers in particular frequently deal with incredibly complex supply streams that face a near-certainty of disruption. Managing complex relationships between suppliers, shippers, and production processes can lead planners to the brink of numerous potential pitfalls, but, luckily, in the era of Industry 4.0 there are more tools than ever designed to alleviate the pain points of the past.

Today’s blog entry features thoughts and insights on the connected nature of Industry 4.0 and increased supply chain visibility and agility from Shay Sidner, flexis North America, Inc’s Director of Operations (pictured middle). As a respected thought-leader in the supply chain industry with more than 10 years experience in supply chain software and optimization, here Shay speaks in her own words about how Industry 4.0 connects to visibility and how this development in planning and production programs is the engine which drives modern manufacturing processes.

We discussed in a recent blog post the industry-wide debate on the long-term, disruptive power of Big Data in today’s manufacturing landscape. Essentially, the debate boiled down as to whether Big Data was actually a value proposition manufacturing companies could depend on into the future as opposed to a flash-in-the-pan phenomenon that may simply fade as new supply chain management theories and philosophies come into view. While it was clear from our examination Big Data is here to stay, there’s still much to understand about how exactly Big Data fits into an integrated supply stream.

For example, how should manufacturing companies leverage the information, data, analytics, and predictive insights created via Big Data into a competitive advantage? Or, what benefits does Big Data provide planners and managers in terms of reducing supply chain management risks? Lastly, how does Big Data impact the entire production cycle from planning and procurement to transport management and customer relations?

If our goal on the flexis AG blog to educate our readers about the pressing issues in global manufacturing and supply chain management, then today’s entry is right on par with that mission. Transport logistics, though a critical element to a manufacturing supply chain management strategy, is perhaps one of the least discussed aspects of SCM. While an underutilized element of administering a successful value chain, transport logistics (or the manner in which companies move finished products from the production room floor to the customer’s door) is the last crucial link in fulfilling customer expectations and ensuring production programs are executed to their fullest extent.

It’s somewhat difficult to understand why transport logistics often gets lost in the fray of global supply chain management. Perhaps it’s because more emphasis is placed on operations at earlier stages in the value chain such as planning and procurement. Or perhaps it’s because the facilitating of effective production programs is often at the forefront of the minds of planners and managers. Either way, transport logistics, though often neglected, can either be a significant boon or detriment to how effective a manufacturing company conducts itself.

One of the greatest challenges manufacturing companies face in today’s global, competitive landscape is generating and sustaining growth of revenue. Because the manufacturing industry is variant-rich and often reliant on complex partner networks spread across the globe, manufacturing companies often operate in a ‘lean and mean’ context where profits can be fairly small and margins for error in terms of investment and return are razor thin. As a result, companies must deploy a number of concepts, platforms, and campaigns to help bolster revenue, optimize processes, and eliminate instances of waste or redundancy.

There’s one thing that pops to mind when you ask people about artificial intelligence in the manufacturing industry, particularly in the automotive landscape: Autonomous or driver-assisted cars. And the future of driverless vehicles may in fact arrive sooner than many people think, there’s actually more concrete contexts for artificial intelligence (AI) in today’s manufacturing sphere. AI has the potential to impact the automotive manufacturing supply chain in equally profound and interesting ways beyond the idea of the driverless car. In fact, AI has the potential to be a truly disruptive force in the way automotive manufacturing companies produce vehicles and how the consumer interacts with the end product. 

With AI as an increasingly common technology platform, the manufacturing industry — automotive sector in particular — is set to experience significant changes in the coming years in terms of production and supply chain management. As vehicles become more integrated, individualized, and complex, manufacturing companies will have to leverage more lean methods of production and supply chain logistics to keep pace with the demands of such a variant-rich industry.