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.
It’s been said that we should think of scientific revolutions not as revolutions per se, but as paradigm shifts—meaning that, rather than thinking of the great breakthroughs in 20th century physics or medicine as groundbreaking seismic shifts, we should consider them in terms of reorientations of method and changing understandings of old knowledge. The same might well be said of new developments in industry. The rise of automation, for instance, didn’t do away with the use of manpower overnight. Instead, it led us to reconsider the way we utilize people as resources and the way that we structure processes around manual intervention.
What does this way of thinking mean for how we discuss “the fourth industrial revolution,” i.e. Industry 4.0? Simply put, the tremendous potential benefits of Industry 4.0 won’t happen on their own. Yes, manufacturing as a field will change drastically and factories will become smarter and more reliant on sensors and internet of things (IoT) devices, but companies need to make an active engagement with these changes by learning to rethink their processes and their use of resources across the supply chain. This raises an important question: how can companies make the most of this new paradigm shift?
Even with the continuing rise of Industry 4.0, many companies treat transportation scheduling as something of an afterthought. Sure, many businesses have restocking rules and recurring transportation orders that are carried out on identical timetables every set number of weeks or months, but today relatively few manufacturers employ a truly robust solution for scheduling transportation. We’ve spent time on this blog touting the importance of transport logistics, but ours is obviously not the only opinion on the subject. Let’s take a few minutes to discuss some of the potential arguments used against it.
As the year draws to a close, we at flexis look forward to a new year's worth of innovation and insight as we renew our commitment to keeping readers informed on the new challenges and solutions that define the ever-changing world of supply chain management. 2018 promises to be even more exciting than its predecessor and we promise to help our readers stay up to date on the arising trends that may impact their businesses.
The title of this blog says it all. Planning silos, even in today’s fairly integrated, optimized supply stream, are still a major challenge for manufacturing companies, especially in variant-rich industries with complex partner-networks. The prospect of cross-organizational communication and data-sharing in the planning stage of the production cycle remains for too many companies simply that: A prospect, a goal, rather than a standard mode of operation.
But for manufacturing companies who understand and realize its value, digitization can be a critical (or perhaps the critical) tool in eliminating these planning silos and fostering an atmosphere of communication and collaboration during the production planning process. Whether it’s constructing a more efficient, streamlined planning and production scheme or creating enhanced methods of procurement, inventory management, job allocation, and transport logistics, digitization is a supply chain management platform whereby companies can leverage greater efficacy to grow their business, create stronger partner networks, and leverage competitive advantages in an increasingly crowded marketplace.