Let’s say you’re moving into a new home. After a long day of loading boxes and furniture into your car and driving them to your new place of residence you’ve finally transported everything into your new house and you’re almost ready to start unpacking and get the place settled. Before you get down to the work of opening up all of your packed boxes, you realize that you haven’t eaten all day, and should probably whip something up before you do any more manual labor. It should be pretty easy to find your kitchen supplies and snack foods, because you made a list of what was in each box before you moved them. One problem: you don’t know which box the list is in. 

In order to remain competitive in the world of modern manufacturing, production planners are constantly searching for new ways to derive more value from their operations. This impulse takes many forms, but one of the most common is striving to improve operational capacities, usually by either reducing makespan or improving machine utilization. Though the obvious benefits of increasing your throughput may seem tantalizing, the process of actually doing so is not as simple as ratcheting up production speed or buying new machines. Rather, it is a complex process that requires a high degree of visibility into your value stream. To help you tackle these complexities, here are 5 key strategies for improving operational capacities.  

Imagine a world in which trillions of individual pieces of information are gathered each day to create complex predictions about future supply chain disruptions and events. Extremely granular data on trade markets turns information about the movement of goods and services throughout the globe into cognitive systems with the power to illuminate new possibilities and intelligently predict changes in demand before they occur. While this may sound like science fiction, it’s increasingly becoming a reality as supply chains become more and more integrated with machine learning, artificial intelligence, and big data analytics. By 2020, IDC predicts that 50% of supply chains will utilize advanced analytics and artificial intelligence, and the effects on the global supply chain are sure to be widespread. 

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?