Though the industrial revolution was nominally about the introduction of steam powered machinery into factory production, its long-term effects are almost impossible to overstate. What began as an ingenious change to the inner workings of factories became a catalyst for widespread social and political change, arguably leading to the formation of modern capitalism and paving the way for a fundamental redefinition of people’s relationships to labor, their environments, and each other. Though the so-called second and third industrial revolutions were not quite as earth-shattering, they did stimulate the global spread of electricity and the internet, two technologies without which the modern world would be virtually unrecognizable.
Industry 4.0, also known as the Fourth Industrial Revolution, has been hailed as the underpinning of the modern smart factory, promoting the rise of cyber-physical systems, increased machine-to-machine communication, and decentralized decision-making within production processes. The concepts that make up the Industry 4.0 framework have been suitably revolutionary, and they're rapidly changing the way that manufacturing businesses operate, but many organizations are realizing that this framework doesn’t have to stop at the edge of the factory floor. Indeed, the very same principles that drive modern, digitized manufacturing are also bringing about the era of Logistics 4.0.
Imagine for a moment that you’re at an antiques auction. You’ve scoped out a handful of items that might meet your needs, and you have a strong but flexible sense of how much money you would be willing to spend on any given item. But when the first of your lots is on the auction block, instead of sitting in the auction house, you’re situated at a remote location, watching a live video feed of the proceedings. When you want to place a bid, you have to instruct your representative at the auction house to raise her paddle. Naturally, by the time you’ve done this, the price that you’re acting on is already out of date. As a result, you wind up with none of the items you had hoped for, even in cases where you might have been willing to spend more on them than the price that they ultimately went for.
According to McKinsey’s estimates, the rise of the Internet-of-Things (IoT) will have more than a $11 trillion economic impact within the next 7 years. Much of this value will come in the rapidly evolving world of connected consumer goods, such as the internet-enabled products that make up the modern smart home, but the impact will also be felt widely in a number of industries, from health care to natural gas production to, of course, automotive manufacturing. We’ve spoken briefly on this blog about the application of IoT devices for tracking inventory usage and traffic patterns, but what impact will this explosion of connected devices have on factory production processes themselves? More to the point, how can you leverage them into meaningful value propositions within your business’ existing workflows.
In industrial, shipping, and freight forwarding sectors, equipment breakdowns are simply a fact of life. That said, unplanned machine outages or vehicle breakdowns can have wide-ranging impacts throughout a given company’s entire value stream, negatively impacting production schedules, transport routing, and capacity management. IndustryWeek estimates that across the world of manufacturing, as much as $55 billion is lost annually to unplanned maintenance time, with some businesses losing up to $22 thousand per minute of machine downtime—meaning that any solution that can decrease the number of unplanned outages represents a significant value added proposition with the ability to decrease overall supply chain volatility.
If there’s one thing today’s planners and managers wish they had to ensure their planning and production strategies, it would be a crystal ball. A magical ability to glimpse into the future in order cut the complexity and uncertainty of modern manufacturing and provide a path of stability and certainty in a variant-rich value stream. While a crystal ball is obviously an impossibility, planners and managers do have a critical tool to help predict future planning and production needs while at the same time managing inventory levels and job allocation strategies for maximum efficiency and productivity.
With a name like “intelligent planning,” it’s hard to imagine that many companies would express a strong preference to do the opposite. And yet, despite intelligent planning’s status as a potential value-added proposition with the ability to smooth out production and transport workflows, many businesses have been slow to implement smarter scheduling and operational planning processes. The reason for this is simple: many modern manufacturers are stuck in the past when it comes to data visibility and planning workflows. Production plans created with pen and ink or Excel spreadsheets can never provide the level of agility, flexibility, or transparency that a lean supply chain requires, but many companies’ planning workflows are unable to evolve do to widespread planning silos and shadow IT.
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.
In a recent research report, Business Insider found that when it came to machine learning, 53% of the company executives surveyed were interested in the emerging technology, but unclear as to its exact use cases and applications. Similar figures applied to executive attitudes towards other technological advances, such as artificial intelligence and 3D printing. Although machine learning in particular is already driving new Industry 4.0 workflows and fundamentally changing the way that manufacturers do business, it’s no surprise that many have trouble envisioning specific applications for it. The transformative power of new technological advances comes not from generalities, but from specific tools and methods for integration that must be carefully calibrated to specific business functions.
Imagine for a moment that your company is in the businesses of manufacturing parts for both conventional and hybrid vehicles. Based on the demand from your customers over the past year or multiple years, you develop a plan for allocating resources and person-hours to produce the right proportion of hybrid parts to conventional parts based on expected demand. Unexpectedly, a sharp increase in worldwide oil prices triggers a shift in demand away from hybrids and toward conventional automobiles that rely on gasoline. How will your production plans cope with the sudden change? Will your factory floors continue to produce a surplus of hybrid parts while orders for conventional parts go un-filled, or do you have the necessary planning agility to shift production to align with new demand paradigms?
Hopefully, you are in a position to safely assume the latter. But for many complex businesses, rapidly adjusting to demand is a perilously involved tasked, requiring the ability to assess and respond to new circumstances virtually instantaneously. One of the keys to building this level of agility into production processes is the integration of real-time and production planning.