Imagine for a moment that you’re planning a camping trip with your friends. There are several of you, and the trip will last a few days, meaning that you’re going to have to take two cars and considerable volume of supplies. How do you decide how each car will be filled? Let’s say your friend already has tent poles and fire starting material, so it might fall to you to procure and transport sleeping bags and food supplies. If one car is more fuel efficient than the other, does that change your plans? How will you go about choosing the right route to your destination in order to find the right balance between toll roads and potentially less direct pathways?
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.
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.
We discuss in great detail on this blog how integrated processes and optimized models result in enhanced operations, increased productivity, and more effective strategic vision. While these are certainly critical and worthy elements of discussion, they are part and parcel to a much larger concern we devote little conversation to: How these various planning and production techniques actually result in a more innovative way of doing business. Because, at the end of the day, a manufacturing company is a business, and a software solution or platform is only as valuable insofar as it helps a business develop and grow.
For example, take the idea of integrated production planning. Such a planning method is a core driver in helping today’s manufacturing companies (especially those in variant-rich industries such as automotive or packaging) not only reduce costs, but also create inroads for revenue generation and growth across the value stream.
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?
In chess, players are taught to think at least three moves ahead. Every action in the game has a reaction, which can be predicted only to a certain extent, and each possible reaction must be planned for in order to efficiently execute a winning strategy. If each piece on the board represents mission critical resources and manpower, then your short- and mid-term planning must take a holistic account of the board and the structure of the game into account in order to be certain that time and resources are not wasted.
It’s safe to say Big Data is here to stay. Since its introduction in the manufacturing landscape in the early 1990’s, Big Data has demonstrated its value proposition in the capacity for grouping, sorting, and analyzing large and complex data sets into executable actions, provides planners and managers the capability to apply predictive analytics and other forward-looking logistic strategies to increase the efficacy, efficiency, and cost-effectiveness of planning and production programs.
Big Data has since found a home working in tandem with other supply and manufacturing movements such as Industry 4.0, Advanced Analytics, and The Internet of Things (IoT). Alongside these technological developments and platforms, Big Data has helped companies gain increased insight and visibility into a number of critical planning and production functions such as forecasting, modeling, data analysis, and the implementation of integrated sales and manufacturing principles for a more streamlined production cycle.
Intelligent production programs begin well before materials hit the production room floor. Even with an optimized production sequence with the latest in production technology platforms, today’s manufacturing companies cannot fully realize an efficient manufacturing cycle without a transparent, agile intelligent planning architecture.
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.
Machines are nothing new to the manufacturing industry - in fact, to say that is quite an understatement. Since the Industrial Revolution, the production facility floor has ground zero for how manufacturing companies incorporate non-human elements or intervention into how goods are produced and distributed. Fast-forward to today’s manufacturing landscape and the introduction and proliferation of modern machine-based aspects such as robotics or artificial intelligence to streamline production processes and increase production efficiency is perhaps the most pressing, pertinent issue in modern production processes.
But what’s slowly gaining more and more prominence in the manufacturing industry is machine learning outside of the actual production space and the ways in which a digitized manufacturing platform can enhance both the production and logistics side of global supply chain management. Understanding machine learning in this context — a holistic reimagination of how this technology can be a disruptive force in a cross-organizational way from sales and procurement to transport logistics — puts machine learning on a grander stage in terms of shaping the future of the automotive supply chain. In addition, machine learning can provide planners and managers with a critical competitive advantage in a somewhat uncertain, variant-rich manufacturing space.